Publications

@article{doi:10.1080/23744731.2024.2351311,

title = {Evaluation of data imputation approaches for multi-stream building systems data1},

author = {David Hälleberg Jin Wen Ojas Pradhan and Zheng O’Neill},

url = {https://doi.org/10.1080/23744731.2024.2351311},

doi = {10.1080/23744731.2024.2351311},

year  = {2024},

date = {2024-01-01},

journal = {Science and Technology for the Built Environment},

volume = {0},

number = {0},

pages = {1–14},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Granderson2023,

title = {A labeled dataset for building HVAC systems operating in faulted and fault-free states},

author = {Jessica Granderson and Guanjing Lin and Yimin Chen and Armando Casillas and Jin Wen and Zhelun Chen and Piljae Im and Sen Huang and Jiazhen Ling},

doi = {10.1038/s41597-023-02197-w},

issn = {2052-4463},

year  = {2023},

date = {2023-12-00},

journal = {Sci Data},

volume = {10},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractOpen data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020.},

keywords = {Computer Science Applications, Education, Information Systems, Library and Information Sciences, Probability and Uncertainty, Statistics, Statistics and Probability},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2023,

title = {A review of data-driven fault detection and diagnostics for building HVAC systems},

author = {Z. Chen and Z. O'Neill and J. Wen and O. Pradhan and T. Yang and X. Lu and G. Lin and S. Miyata and S. Lee and C. Shen and R. Chiosa and M. S. Piscitelli and A. Capozzoli and F. Hengel and A. Kührer and M. Pritoni and W. Liu and J. Clauß and Y. Chen and T. Herr},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151031548&doi=10.1016%2fj.apenergy.2023.121030&partnerID=40&md5=e5945a349e25895c79ae61a83e84346d},

doi = {10.1016/j.apenergy.2023.121030},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Applied Energy},

volume = {339},

abstract = {With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD. © 2023 Elsevier Ltd},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2022,

title = {From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control},

author = {Tao Yang and Arkasama Bandyopadhyay and Zheng O'Neill and Jin Wen and Bing Dong},

url = {https://doi.org/10.1007/s12273-021-0861-0},

doi = {10.1007/s12273-021-0861-0},

issn = {1996-8744},

year  = {2022},

date = {2022-06-01},

journal = {Building Simulation},

volume = {15},

number = {6},

pages = {913-932},

abstract = {Occupants are the core of the built environment. Traditional heating, ventilation, and air-conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC ``Occupant-centric Control (OCC).'' HVAC OCC strategy starts with collecting the occupant's information (e.g., presence/absence) and then applies it to meet the occupant's requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. We provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2022,

title = {Evaluating the performance of an Inexact Newton method with a preconditioner for dynamic building system simulation},

author = {Zhelun Chen and Jin Wen and Anthony J. Kearsley and Amanda Pertzborn},

url = {https://doi.org/10.1080/19401493.2021.2007285},

doi = {10.1080/19401493.2021.2007285},

issn = {1940-1493},

year  = {2022},

date = {2022-01-02},

journal = {Journal of Building Performance Simulation},

volume = {15},

number = {1},

pages = {112-127},

publisher = {Taylor & Francis},

abstract = {Efficiently, robustly, and accurately solving systems of nonlinear differential algebraic equations for dynamic building system simulation is becoming more important due to the increasing need to simulate large-scale problems. The focus of this paper is an investigation of methods for solving the nonlinear algebraic equations encountered in dynamic building system simulations. Dynamic building system simulations employ a myriad of solution techniques, many of which use derivative information. As the problem size grows, so do the memory requirements, leading to challenges in solving large-scale problems. Newton-Krylov methods are a promising candidate for large-scale simulation. The performance of these methods is often improved, sometimes drastically, when employed with a preconditioning technique. Here, a cost-effective preconditioning technique is applied to a Newton-Krylov method and tested on a series of problems. To illustrate the benefits of the approach, comparison is made to the frequently employed Powell?s Hybrid Method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AWADA2022108440,

title = {Occupant health in buildings: Impact of the COVID-19 pandemic on the opinions of building professionals and implications on research},

author = {Mohamad Awada and Burçin Becerik-Gerber and Elizabeth White and Simi Hoque and Zheng O'Neill and Giulia Pedrielli and Jin Wen and Teresa Wu},

url = {https://www.sciencedirect.com/science/article/pii/S0360132321008362},

doi = {https://doi.org/10.1016/j.buildenv.2021.108440},

issn = {0360-1323},

year  = {2022},

date = {2022-01-01},

journal = {Building and Environment},

volume = {207},

pages = {108440},

abstract = {The objectives of this study are to investigate building professionals' experience, awareness, and interest in occupant health in buildings, and to assess the impact of the COVID-19 pandemic on their opinions, as well as to compare the research on occupant health in buildings to professionals' opinions. To address these objectives, a mixed research methodology, including a thorough review of the literature (NL = 190) and an online survey (NS = 274), was utilized. In general, there is an increasing research interest in occupant health and a heightened interest in health-related projects, among professionals, following the COVID-19 pandemic. Specifically, among the nine different building attributes examined, indoor air quality was the most researched building attribute with a focus on occupant health and was also presumed to be the most important by the professionals. Professionals considered fatigue and musculoskeletal pain to be the most important physical well-being issues, and stress, anxiety, and depression to be the most important mental well-being issues that need to be the focus of design, construction, and operation of buildings to support and promote occupant health, while eye-related symptoms and loss of concentration were the most researched physical and mental well-being symptoms in the literature, respectively. Finally, professionals indicated that COVID-19 pandemic had significant effect on their perspectives regarding buildings’ impact on occupant health and they believed future building design, construction and operation will focus more on occupant health because of the pandemic experience.},

keywords = {Buildings, COVID-19, Health, Occupants, Professionals, State-of-the-art},

pubstate = {published},

tppubtype = {article}

}

@article{CHEN2022112041,

title = {A simulation-based evaluation of fan coil unit fault effects},

author = {Yimin Chen and Guanjing Lin and Zhelun Chen and Jin Wen and Jessica Granderson},

url = {https://www.sciencedirect.com/science/article/pii/S0378778822002122},

doi = {https://doi.org/10.1016/j.enbuild.2022.112041},

issn = {0378-7788},

year  = {2022},

date = {2022-01-01},

journal = {Energy and Buildings},

volume = {263},

pages = {112041},

abstract = {Faults in heating, ventilation and air conditioning (HVAC) systems cause increased energy consumption, degrading thermal comforts, growing operational cost and reduced system lifespan. An effective evaluation of fault effects is critical to the development of various fault diagnostics solutions, the improvement of operation maintenance and the optimization of monitoring systems. In the HVAC area, a majority of research work in evaluating fault effects was to analyze energy consumption impacts or thermal comfort impacts. However, a handful of research has been conducted on evaluating fault effects on various measurements, which are increasingly employed to monitor equipment's operation. Fault effects on various measurements may display different symptom patterns and present changed sensitivities when the equipment operates under various faults, severity levels, as well as operation conditions. However, a long-term observation of fault symptoms under various operation conditions, different fault types and severity levels to evaluate fault effects is extremely challenging. In this paper, a simulation-based framework was proposed to evaluate fault effects in fan coil units (FCUs). Two metrics namely fault symptom occurrence probability (SOP) and fault symptom daily continuous duration (SDCD) were developed to quantify fault symptoms under various FCU faults. A total of 18 common FCU faults at different severity levels were implemented on the developed HVACSIM+ simulation platform to obtain a full year fault inclusive data set for 48 fault simulation cases. The framework, as well as obtained SOP and SDCD distributions will benefit multiple folds such as the development of probability-based fault diagnostics inference approaches, optimization of sensor location, and fault prioritization.},

keywords = {Fan Coil unit, Fault effects, Fault symptom evaluation, HVACSIM+, Symptom intensity, Symptom occurrence probability},

pubstate = {published},

tppubtype = {article}

}

@article{HUANG2022111872,

title = {Real vs. simulated: Questions on the capability of simulated datasets on building fault detection for energy efficiency from a data-driven perspective},

author = {Jiajing Huang and Jin Wen and Hyunsoo Yoon and Ojas Pradhan and Teresa Wu and Zheng O'Neill and Kasim Selcuk Candan},

url = {https://www.sciencedirect.com/science/article/pii/S0378778822000433},

doi = {https://doi.org/10.1016/j.enbuild.2022.111872},

issn = {0378-7788},

year  = {2022},

date = {2022-01-01},

journal = {Energy and Buildings},

volume = {259},

pages = {111872},

abstract = {Literature on building Automatic Fault Detection and Diagnosis (AFDD) mainly focuses on simulated system data due to high expenses and difficulties of obtaining and analyzing real building data. There is a lack of validation on performances and scalabilities of data-driven AFDD approaches using simulated data and how it compares to that from real building data. In this study, we conduct two sets of experiments to seek answers to this question. We first evaluate data-driven fault detection strategies on real and simulated building data separately. We observe that the fault detection performances are not affected by fault detection strategies, sizes of training data, and the number of cross-validation folds when training and blind test data come from the same data source, namely, simulated or real building data. Next, we conduct a cross-dataset study, that is, develop the model using simulated data and tested on real building data. The results indicate the model trained on simulated data is not generalized to be applied for real building data for fault detection. Kolmogorov-Smirnov Test is conducted to confirm that there exist statistical differences between the simulated and real building data and identify a subset of features with similarities between the two datasets. Using the subset of the feature, cross-dataset experiments show fault detection improvements on most fault cases. We conclude that even if the system produces simulated data with the same fault symptoms from physical analysis perspectives, not all features from simulated datasets may not be beneficial for AFDD but only a subset of features contains valuable information from a machine learning perspective.},

keywords = {Building AFDD, Machine learning, Real, Similarity, Simulated},

pubstate = {published},

tppubtype = {article}

}

@article{CHEN2022102401,

title = {Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods},

author = {Chien-fei Chen and Thomas Dietz and Nina H. Fefferman and Jamie Greig and Kristen Cetin and Caitlin Robinson and Laura Arpan and Marcel Schweiker and Bing Dong and Wenbo Wu and Yue Li and Hongyu Zhou and Jianzhong Wu and Jin Wen and Joshua S. Fu and Tianzhen Hong and Da Yan and Hannah Nelson and Yimin Zhu and Xueping Li and Le Xie and Rachel Fu},

url = {https://www.sciencedirect.com/science/article/pii/S2214629621004886},

doi = {https://doi.org/10.1016/j.erss.2021.102401},

issn = {2214-6296},

year  = {2022},

date = {2022-01-01},

journal = {Energy Research & Social Science},

volume = {85},

pages = {102401},

abstract = {Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.},

keywords = {COVID-19, Disasters, Energy inequality, Energy insecurity, Energy justice, Resilience},

pubstate = {published},

tppubtype = {article}

}

@article{dfd328ce82724d62bd5d30f001852d66,

title = {A cosine-based correlation information entropy approach for building automatic fault detection baseline construction},

author = {Jiajing Huang and Hyunsoo Yoon and Ojas Pradhan and Teresa Wu and Jin Wen and Zheng O’neill and Kasim Selcuk Candan},

doi = {10.1080/23744731.2022.2080110},

issn = {2374-4731},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Science and Technology for the Built Environment},

volume = {28},

number = {9},

pages = {1138--1149},

publisher = {Taylor and Francis Ltd.},

abstract = {Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. In addition, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.},

note = {Funding Information: We gratefully thank DOE CYDRES Project (Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)) for support for this work. Publisher Copyright: textcopyright Copyright textcopyright 2022 ASHRAE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CHEN2022120050,

title = {Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems},

author = {Yimin Chen and Jin Wen and Ojas Pradhan and L. James Lo and Teresa Wu},

url = {https://www.sciencedirect.com/science/article/pii/S0306261922013071},

doi = {https://doi.org/10.1016/j.apenergy.2022.120050},

issn = {0306-2619},

year  = {2022},

date = {2022-01-01},

journal = {Applied Energy},

volume = {327},

pages = {120050},

abstract = {Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. This paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.},

keywords = {Cross-level fault, Discrete Bayesian Network, HVAC system, Pattern Matching, Root cause fault diagnosis},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2021,

title = {Using Weather and Schedule Based Pattern Matching and Feature Based Principal Component Analysis for Whole Building Fault Detection---Part II Field Evaluation},

author = {Yimin Chen and Jin Wen and James Lo},

url = {https://doi.org/10.1115/1.4052730},

doi = {10.1115/1.4052730},

issn = {2642-6641},

year  = {2021},

date = {2021-12-14},

journal = {ASME Journal of Engineering for Sustainable Buildings and Cities},

volume = {3},

number = {1},

abstract = {In a heating, ventilation, and air conditioning (HVAC) system, a whole building fault (WBF) refers to a fault that occurs in one component but may trigger additional faults/abnormalities on different components or subsystems resulting in significant impacts on the energy consumption or indoor air quality in buildings. At the whole building level, interval data collected from various components/subsystems can be used to detect WBFs. In the Part I of this study, a novel data-driven method which includes weather and schedule-based pattern matching (WPM) procedure and a feature based principal component analysis (FPCA) procedure was developed to detect the WBF. This article is the second of a two-part study of the development of the whole building fault detection method. In the Part II of the study (this paper), various WBFs were designed and imposed in the HVAC system of a campus building. Data from both imposed fault and naturally occurred faults were collected through the building automation system (BAS) to evaluate the developed fault detection method. Evaluation results show that the developed WPM-FPCA method reaches a satisfactory detection rate (85% and 100% under two principal component retention rates) and a 0% false alarm rate (under two principal component retention rates).},

note = {011002},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2021b,

title = {Using Weather and Schedule-Based Pattern Matching and Feature-Based Principal Component Analysis for Whole Building Fault Detection---Part I Development of the Method},

author = {Yimin Chen and Jin Wen and James Lo},

url = {https://doi.org/10.1115/1.4052729},

doi = {10.1115/1.4052729},

issn = {2642-6641},

year  = {2021},

date = {2021-11-09},

journal = {ASME Journal of Engineering for Sustainable Buildings and Cities},

volume = {3},

number = {1},

abstract = {A whole building fault (WBF) refers to a fault occurring in one component, but may cause impacts on other components or subsystems, or arise significant impacts on energy consumption and thermal comfort. Conventional methods (such as component level rule-based method or physical model-based method) which targeted at component level fault detection cannot be successfully used to detect a WBF because of the fault propagation among the closely coupled equipment or subsystems. Therefore, a novel data-driven method named weather and schedule-based pattern matching (WPM) and feature-based principal component analysis (FPCA) method for WBF detection is developed. Three processes are established in the WPM-FPCA method to address three main issues in WBF detection. First, a feature selection process is used to pre-select data measurements which represent a whole building's operation performance under a satisfied status, namely, baseline status. Second, a WPM process is used to locate weather and schedule patterns in the historical baseline database, which are similar to that from the current/incoming operation data, and to generate a WPM baseline. Lastly, real-time PCA models are generated for both the WPM baseline data and the current operation data. Statistic thresholds used to differentiate normal and abnormal (faulty) operations are automatically generated in this PCA modeling process. The PCA models and thresholds are used to detect the WBF. This paper is the first of a two-part study. Performance evaluation of the developed method is conducted using data collected from a real campus building and will be described in the second part of this paper.},

note = {011001},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pang2021,

title = {How Work From Home Has Affected the Occupant's Well-Being in the Residential Built Environment: An International Survey Amid the Covid-19 Pandemic},

author = {Zhihong Pang and Burçin Becerik-Gerber and Simi Hoque and Zheng O'Neill and Giulia Pedrielli and Jin Wen and Teresa Wu},

url = {https://doi.org/10.1115/1.4052640},

doi = {10.1115/1.4052640},

issn = {2642-6641},

year  = {2021},

date = {2021-10-26},

journal = {ASME Journal of Engineering for Sustainable Buildings and Cities},

volume = {2},

number = {4},

abstract = {This paper presents the results from an international survey that investigated the impacts of the built environment on occupant well-being during the corona virus disease 2019 (COVID-19) pandemic when most professionals were forced to work from home (WFH). The survey was comprised of 81 questions focusing on the respondent's profiles, residences, home indoor environmental quality, health, and home working experiences. A total of 1460 responses were collected from 35 countries, and 1137 of them were considered complete for the analysis. The results suggest that home spatial layout has a significant impact on occupant well-being during WFH since home-life distractions and noises due to the lack of a personal workspace are likely to prevent productive work. Lack of scenic views, inadequate daylighting, and poor acoustics were also reported to be detrimental to occupant productivity and the general WFH experience. It is also revealed from this survey that temperature, relative humidity, and indoor air quality generally have higher satisfaction ratios compared with the indoor lighting and acoustic conditions, and the home layout. Hence, home design for lighting, acoustics, and layout should also receive greater attention in the future.},

note = {041003},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AWADA2021107480,

title = {Ten questions concerning occupant health in buildings during normal operations and extreme events including the COVID-19 pandemic},

author = {Mohamad Awada and Burcin Becerik-Gerber and Simi Hoque and Zheng O'Neill and Giulia Pedrielli and Jin Wen and Teresa Wu},

url = {https://www.sciencedirect.com/science/article/pii/S0360132320308477},

doi = {https://doi.org/10.1016/j.buildenv.2020.107480},

issn = {0360-1323},

year  = {2021},

date = {2021-01-01},

journal = {Building and Environment},

volume = {188},

pages = {107480},

abstract = {Even before the COVID-19 pandemic, people spent on average around 90% of their time indoors. Now more than ever, with work-from-home orders in place, it is crucial that we radically rethink the design and operation of buildings. Indoor Environmental Quality (IEQ) directly affects the comfort and well-being of occupants. When IEQ is compromised, occupants are at increased risk for many diseases that are exacerbated by both social and economic forces. In the U.S. alone, the annual cost attributed to sick building syndrome in commercial workplaces is estimated to be between $10 billion to $70 billion. It is imperative to understand how parameters that drive IEQ can be designed properly and how buildings can be operated to provide ideal IEQ to safeguard health. While IEQ is a fertile area of scholarship, there is a pressing need for a systematic understanding of how IEQ factors impact occupant health. During extreme events, such as a global pandemic, designers, facility managers, and occupants need pragmatic guidance on reducing health risks in buildings. This paper answers ten questions that explore the effects of buildings on the health of occupants. The study establishes a foundation for future work and provides insights for new research directions and discoveries.},

keywords = {Buildings, COVID-19 pandemic, Extreme events, Health, Indoor environmental quality, Well-being},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2021116452,

title = {A review of machine learning in building load prediction},

author = {Liang Zhang and Jin Wen and Yanfei Li and Jianli Chen and Yunyang Ye and Yangyang Fu and William Livingood},

url = {https://www.sciencedirect.com/science/article/pii/S0306261921000209},

doi = {https://doi.org/10.1016/j.apenergy.2021.116452},

issn = {0306-2619},

year  = {2021},

date = {2021-01-01},

journal = {Applied Energy},

volume = {285},

pages = {116452},

abstract = {The surge of machine learning and increasing data accessibility in buildings provide great opportunities for applying machine learning to building energy system modeling and analysis. Building load prediction is one of the most critical components for many building control and analytics activities, as well as grid-interactive and energy efficiency building operation. While a large number of research papers exist on the topic of machine-learning-based building load prediction, a comprehensive review from the perspective of machine learning is missing. In this paper, we review the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E. Firstly, we review the applications of building load prediction model (task T). Then, we review the modeling algorithms that improve machine learning performance and accuracy (performance P). Throughout the papers, we also review the literature from the data perspective for modeling (experience E), including data engineering from the sensor level to data level, pre-processing, feature extraction and selection. Finally, we conclude with a discussion of well-studied and relatively unexplored fields for future research reference. We also identify the gaps in current machine learning application and predict for future trends and development.},

keywords = {Building energy forecasting, Building energy system, Building load prediction, Data engineering, Feature engineering, Machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2021110592,

title = {Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study},

author = {Liang Zhang and Mahmoud Alahmad and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0378778820333788},

doi = {https://doi.org/10.1016/j.enbuild.2020.110592},

issn = {0378-7788},

year  = {2021},

date = {2021-01-01},

journal = {Energy and Buildings},

volume = {231},

pages = {110592},

abstract = {Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.},

keywords = {Building load forecasting, Data-driven modeling, Discrete wavelet transform, Empirical mode decomposition, Noise cancellation, Time–frequency analysis},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2021111026,

title = {Active learning strategy for high fidelity short-term data-driven building energy forecasting},

author = {Liang Zhang and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0378778821003108},

doi = {https://doi.org/10.1016/j.enbuild.2021.111026},

issn = {0378-7788},

year  = {2021},

date = {2021-01-01},

journal = {Energy and Buildings},

volume = {244},

pages = {111026},

abstract = {The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning— an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting. The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.},

keywords = {Active learning, Block design, Building energy forecasting, Expected error reduction, Model predictive control, Training data quality},

pubstate = {published},

tppubtype = {article}

}

@article{LU2021111448,

title = {A holistic fault impact analysis of the high-performance sequences of operation for HVAC systems: Modelica-based case study in a medium-office building},

author = {Xing Lu and Yangyang Fu and Zheng O'Neill and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0378778821007325},

doi = {https://doi.org/10.1016/j.enbuild.2021.111448},

issn = {0378-7788},

year  = {2021},

date = {2021-01-01},

journal = {Energy and Buildings},

volume = {252},

pages = {111448},

abstract = {ASHRAE Guideline 36: High-performance sequences of operation (SOO) for Heating, Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30% energy under ideal simulation environments. However, HVAC systems are susceptible to various types of faults in a real building operation. There are no existing studies that pertain to a comprehensive fault impact analysis of the high-performance control sequences suggested by ASHRAE Guideline 36 for HVAC systems. How these sequences handle and adapt to the various types of faults is still largely unknown. In this context, a comprehensive fault impact analysis and robustness assessment of the high-performance control sequences is conducted. A Modelica-based medium office virtual testbed is developed following the air-side and the plant-side SOO. A total of 359 fault scenarios in three different seasonal operating conditions (cooling, shoulder, and heating seasons) are injected into the baseline model. The evaluated key performance indexes (KPIs) include the operational cost, source energy, site energy, control loop quality, thermal comfort, ventilation, and power system metrics. The faults of the most negative impact are identified for different seasonal operating conditions over all the KPIs. The results also show that high-performance control sequences are well adapted for the vast majority (∼90%) of all the fault scenarios over all the KPIs in this study.},

keywords = {ASHRAE guideline 36, Fault simulation, High-performance control sequences, HVAC, Modelica},

pubstate = {published},

tppubtype = {article}

}

@article{FU2021117639,

title = {Modeling and evaluation of cyber-attacks on grid-interactive efficient buildings},

author = {Yangyang Fu and Zheng O'Neill and Zhiyao Yang and Veronica Adetola and Jin Wen and Lingyu Ren and Tim Wagner and Qi Zhu and Terresa Wu},

url = {https://www.sciencedirect.com/science/article/pii/S0306261921010060},

doi = {https://doi.org/10.1016/j.apenergy.2021.117639},

issn = {0306-2619},

year  = {2021},

date = {2021-01-01},

journal = {Applied Energy},

volume = {303},

pages = {117639},

abstract = {Grid-interactive efficient buildings (GEBs) are not only exposed to passive threats (e.g., physical faults) but also active threats such as cyber-attacks launched on the network-based control systems. The impact of cyber-attacks on GEB operation are not yet fully understood, especially as regards the performance of grid services. To quantify the consequences of cyber-attacks on GEBs, this paper proposes a modeling and simulation framework that includes different cyber-attack models and key performance indexes to quantify the performance of GEB operation under cyber-attacks. The framework is numerically demonstrated to model and evaluate cyber-attacks such as data intrusion attacks and Denial-of-Service attacks on a typical medium-sized office building that uses the BACnet/IP protocol for communication networks. Simulation results show that, while different types of attacks could compromise the building systems to different extents, attacks via the remote control of a chiller yield the most significant consequences on a building system’s operation, including both the building service and the grid service. It is also noted that a cyber-attack impacts the building systems during the attack period as well as the post-attack period, which suggests that both periods should be considered to fully evaluate the consequences of a cyber-attack.},

keywords = {Cyber-attacks, Demand flexibility, Grid-interactive efficient buildings},

pubstate = {published},

tppubtype = {article}

}

@article{Chung2020,

title = {Development and verification of the open source platform, HAM-Tools, for hygrothermal performance simulation of buildings using a stochastic approach},

author = {Daniel Chung and Jin Wen and L. James Lo},

url = {https://doi.org/10.1007/s12273-019-0594-5},

doi = {10.1007/s12273-019-0594-5},

issn = {1996-8744},

year  = {2020},

date = {2020-06-01},

journal = {Building Simulation},

volume = {13},

number = {3},

pages = {497-514},

abstract = {Building envelope design and analysis through simulation tools are areas of research and professional practice within the architecture, engineering, and construction (AEC) industries that can have substantial economic outcomes. Approximately 20% of whole building capital costs are associated with building envelopes. High moisture content within building envelopes is known to promote mold and corrosion while also reducing thermal resistance. Thus, simulating envelope moisture behavior is useful in evaluating designs. To allow for future stochastic and degradation modeling this project has augmented the open source platform, HAM-Tools and verified its results by using WUFI Pro 6.1 software. HAM-Tools is a robust one-dimensional H.A.M. analysis software using MATLAB and Simulink computational environments which allows for further development and research. In this work, wind-driven rain, rain penetration, as well as heat & moisture sources in air layers have been added to HAM-Tools. The paper compares the results from HAM-Tools and WUFI for a set of common ventilated cladding scenarios. Insulation degradation (which cannot be analyzed in WUFI) is also integrated into HAM-Tools and moisture content is simulated over a 10-year period to demonstrate how the platform can be used to examine long term moisture impact. The results of the study show that HAM-Tools and WUFI can produce relatively close results for moisture content within the envelope given the same ventilated scenarios. The 10-year studies with and without insulation degradation show that there are times where there are significant differences in the moisture content predicted with and without insulation degradation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chung2019,

title = {Building Envelope Moisture Transport in the Context of Assembly Aging and Uncertainty},

author = {Daniel Chung and Jin Wen},

url = {https://doi.org/10.1080/24751448.2019.1640540},

doi = {10.1080/24751448.2019.1640540},

issn = {2475-1448},

year  = {2019},

date = {2019-07-03},

journal = {Technology|Architecture + Design},

volume = {3},

number = {2},

pages = {221-233},

publisher = {Routledge},

abstract = {Uncertainty analysis and assembly aging can be meaningfully integrated into building envelope simulations to predict changes to heat and moisture transport when compared with typical non-probabilistic and nontime-dependent envelope analysis. A literature review of material durability, building envelope design for energy optimization, and longitudinal thermal performance is included to explore uncertainty analysis for building envelopes in current uses. Probabilistic inputs and time-varying material parameters are proposed as a method and tested on a typical wood-framed residential exterior wall through simulation. The results show that typical analysis underrepresents the risk of condensation in nearly all configurations when compared to probabilistic expected values. The location of additional insulation, using stochastic inputs and including degradation, can impact predicted condensation within envelope assemblies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HENDRICKEN2019287,

title = {Development of a new reduced order model for predicting the energy savings of multi-ECM permutations},

author = {Liam Hendricken and Jin Wen and Patrick L.Gurian},

url = {https://www.sciencedirect.com/science/article/pii/S0378778818330500},

doi = {https://doi.org/10.1016/j.enbuild.2018.10.028},

issn = {0378-7788},

year  = {2019},

date = {2019-01-01},

journal = {Energy and Buildings},

volume = {182},

pages = {287-299},

abstract = {Building performance simulation (BPS) enables users to predict the demand reductions achieved by energy conservation measures (ECMs). Identifying an optimal set of ECMs in combination is complex due to interaction effects. This creates a combinatorial problem where every ECM combination needs be simulated to identify the optimum with certainty. To avoid the computational burden of running separate simulations for each ECM combination, approximate approaches for predicting the joint effects of ECMs based on single ECM simulations have been proposed in literature: linear-addition and log-addition of savings. These reduced-order approaches are very rapid compared to BPS, but their accuracy is not well characterized. This paper compares ECM energy savings estimated by BPS with the linear and log-additive approaches and a new reduced-order approach: log-additive decomposition. An existing library of energy models and ECMs (Hamilton, et al., 2014) representing Philadelphia medium-sized office buildings is utilized to compare the performance of each approach not only to each other but also to BPS. Overall, log-additive decomposition performs well (prediction error ∼10%) followed by log-addition (prediction error ∼20% to 30%), which outperform linear addition (prediction error often exceeding 50%). Compared to BPS, the computational cost of each is 0.018%, 0.014%, and 0.004% respectively.},

keywords = {Building performance simulation, Energy conservation measures, Linear addition, Log-addition, Log-additive decomposition, Reduced-order modeling, Two-way interactions},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2019428,

title = {A systematic feature selection procedure for short-term data-driven building energy forecasting model development},

author = {Liang Zhang and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0378778818321625},

doi = {https://doi.org/10.1016/j.enbuild.2018.11.010},

issn = {0378-7788},

year  = {2019},

date = {2019-01-01},

journal = {Energy and Buildings},

volume = {183},

pages = {428-442},

abstract = {An accurate building energy forecasting model is the key for real-time model based control of building energy systems and building-grid integration. Data-driven models, though have lower engineering cost during their development process, often suffer from poor model generalization caused by high data dimensionality. Feature selection, a process of selecting a subset of relevant features, can defy high dimensionality, increase model interpretability, and enhance model generalization. In building energy modeling research, features are often selected based on domain knowledge. There lacks a comprehensive methodology to guide a systematic feature selection procedure when developing building energy forecasting models. In this research, a systematic feature selection procedure for developing a building energy forecasting model is proposed which attempts to integrate statistical analysis, building physics and engineering experiences. The proposed procedure includes three steps, i.e., (Step 1) feature pre-processing based on domain knowledge, (Step 2) feature removal through filter methods to remove irrelevant and redundant variables, and (Step 3) feature grouping through wrapper method to search for the best feature set. Two case studies are presented here using both simulated and real building data. The simulated building data are generated from a medium-size office building (a DOE reference building) simulation model. The real building data are obtained from a medium-size campus building in Philadelphia, PA. In both cases, the energy forecasting models that are developed using proposed systematic feature selection procedure is compared with models using other feature selection techniques. Results show that the models developed using proposed procedure have better accuracy and generalization.},

keywords = {Building energy forecasting, Data-driven model, Feature selection},

pubstate = {published},

tppubtype = {article}

}

@article{BENDAVID2019106314,

title = {Optimizing ventilation: Theoretical study on increasing rates in offices to maximize occupant productivity with constrained additional energy use},

author = {Tom Ben-David and Adams Rackes and L. James Lo and Jin Wen and Michael S. Waring},

url = {https://www.sciencedirect.com/science/article/pii/S0360132319305244},

doi = {https://doi.org/10.1016/j.buildenv.2019.106314},

issn = {0360-1323},

year  = {2019},

date = {2019-01-01},

journal = {Building and Environment},

volume = {166},

pages = {106314},

abstract = {Ventilation affects building energy use and indoor air quality, with minimum rates prescribed by standards. However, research has demonstrated positive outcomes associated with increasing ventilation, including occupant productivity from increased work performance and reduced absenteeism. Herein, a novel ventilation strategy was proposed and simulated for offices, which optimized day-averaged ventilation rates over an annual time horizon to provide maximal amounts of outdoor air and so maximize occupant productive work hours, within varying energy use constraints. Energy use and productivity were often influenced by ventilation oppositely, so results were Pareto optimal. This optimization methodology was simulated in three locations for an average- and high-performance small office building, considering users with varying levels of confidence in ventilation-productivity relationships. To contextualize potential optimization impacts, four annual energy budgets were first determined for a typical year at constant ventilation rates of 8.5, 10, 20, and 30 L/s/occupant, and then for those four cases, day-averaged ventilation rates were optimized over annual trajectories considering the constrained energy budgets. Among all simulated cases, lost productive hours due to lower ventilation at constant rates were halved when using the optimized higher annual rates, with a gain of ~20 h/year per occupant on average, amounting to approximately $48/m2 at standard occupant density and mean wage. Offline optimization results were used to develop heuristic rules to predict a ventilation rate for any single day based on weather forecast that would adhere to a building- and climate-specific Pareto optimization, opening avenues for future control strategies that use this framework in real buildings.},

keywords = {Building energy, Healthy buildings, Occupant performance, Optimization, Ventilation control},

pubstate = {published},

tppubtype = {article}

}

@article{POURARIAN2017151,

title = {A tool for evaluating fault detection and diagnostic methods for fan coil units},

author = {Shokouh Pourarian and Jin Wen and Daniel Veronica and Amanda Pertzborn and Xiaohui Zhou and Ran Liu},

url = {https://www.sciencedirect.com/science/article/pii/S0378778816317698},

doi = {https://doi.org/10.1016/j.enbuild.2016.12.018},

issn = {0378-7788},

year  = {2017},

date = {2017-01-01},

journal = {Energy and Buildings},

volume = {136},

pages = {151-160},

abstract = {Dynamic simulation tools that could accurately simulate operational data for both the fault-free and faulty dynamic operation of heating, ventilation, and air conditioning (HVAC) systems and equipment are needed for developing and evaluating advanced control and automated fault detection and diagnosis strategies. Among various HVAC subsystems, fan coil units (FCUs) are relatively simple, inexpensive devices that are used extensively in commercial, institutional and multifamily residential buildings. However, very little has been reported in the literature to improve FCU design and operation. There has also been a lack of dynamic simulation tool development focusing on FCUs. The work reported in this study aims at developing and validating a software tool to simulate operational data generated from FCUs that are operated dynamically under both faulty and fault-free conditions. A comprehensive and systematic validation process, using data collected from real FCUs in a laboratory building, is used to validate the tool under both faulty and fault-free operating conditions in different seasons. The validated tool not only is able to predict real-world FCU behaviors under different control strategies, but it is also able to predict symptoms associated with various faults, as well as the effects of those faults on system performance and occupant comfort.},

keywords = {AFDD, Fan Coil unit, Fault-free and faulty conditions, Simulation},

pubstate = {published},

tppubtype = {article}

}

@article{LI2017168,

title = {Net-zero energy building clusters emulator for energy planning and operation evaluation},

author = {Xiwang Li and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0198971516302678},

doi = {https://doi.org/10.1016/j.compenvurbsys.2016.09.007},

issn = {0198-9715},

year  = {2017},

date = {2017-01-01},

journal = {Computers, Environment and Urban Systems},

volume = {62},

pages = {168-181},

abstract = {The emergence of smart grids, Net-zero energy buildings, and advanced building energy demand response technologies continuously drives the needs for better design and operation strategies for buildings and distributed energy systems. It is envisioned that similar to micro-communities in a human society, neighboring buildings will have the tendency to form a building cluster, an open cyber-physical system to exploit the economic opportunities provided by smart grids and distributed energy systems. To realize this building cluster envision, it requires better urban energy planning and operation control strategies to determine which type of buildings should be clustered and what operation strategies should be implemented to fully utilize the potential in load aggregation, load shifting, and resource allocation. However, most of the current tools are focusing on single buildings or devices, which are not suitable for building cluster studies. To this end, this study proposes to develop a Net-zero building cluster emulator that can simulate realistic energy behaviors of a cluster of buildings and their distributed energy devices as well as exchange operation data and control schemes with real-world building control systems. The developed emulator has the flexibility to integrate with different buildings and distributed energy systems to study the performance of this building cluster to propose suggestions in urban energy planning and operation. To show the application of this emulator, a proof-of-concept demonstration is also presented in this paper.},

keywords = {Co-simulation, Distributed energy systems, Net-zero building cluster, Net-zero buildings, Smart grids},

pubstate = {published},

tppubtype = {article}

}

@article{Li2016,

title = {Commercial building cooling energy forecasting using proactive system identification: A whole building experiment study},

author = {Xiwang Li and Jin Wen and Ran Liu and Xiaohui Zhou},

url = {https://doi.org/10.1080/23744731.2016.1188654},

doi = {10.1080/23744731.2016.1188654},

issn = {2374-4731},

year  = {2016},

date = {2016-08-17},

journal = {Science and Technology for the Built Environment},

volume = {22},

number = {6},

pages = {674-691},

publisher = {Taylor & Francis},

abstract = {Model-based predictive control has been proven to be a promising solution for improving building energy efficiency and building-grid resilience. High fidelity energy forecasting models are essential to the performance of model predictive controls. The existing energy forecasting modeling principles: physics based (white box), data-driven (black box), and hybrid (gray box) modeling principles all have their own limitations in applying into the real field, such as extensive engineering effort, computation power, and long training periods. Previous studies by the authors presented a novel methodology for energy forecasting model development using system identification approaches based on system characteristics. In this study, whole building experiments are systematically designed and conducted to verify and validate this novel method in a real commercial building. The experimental results demonstrate that the proposed methodology is able to achieve 90% forecasting accuracy within a 1-minute calculation time for chiller energy and total cooling energy forecasting in a 1-day forecasting period under the experimental conditions. A Monte Carlo study also shows that the model is more sensitive to outdoor air temperature and direct solar radiation, but less sensitive to ventilation rate.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Odonkor2016,

title = {Adaptive Energy Optimization Toward Net-Zero Energy Building Clusters},

author = {Philip Odonkor and Kemper Lewis and Jin Wen and Teresa Wu},

url = {https://doi.org/10.1115/1.4033395},

doi = {10.1115/1.4033395},

issn = {1050-0472},

year  = {2016},

date = {2016-05-02},

journal = {Journal of Mechanical Design},

volume = {138},

number = {6},

abstract = {Traditionally viewed as mere energy consumers, buildings have adapted, capitalizing on smart grid technologies and distributed energy resources to efficiently use and trade energy, as evident in net-zero energy buildings (NZEBs). In this paper, we examine the opportunities presented by applying net-zero to building communities (clusters). This paper makes two main contributions: one, it presents a framework for generating Pareto optimal operational strategies for building clusters; two, it examines the energy tradeoffs resulting from adaptive decisions in response to dynamic operation conditions. Using a building cluster simulator, the proposed approach is shown to adaptively and significantly reduce total energy cost.},

note = {061405},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhao2016,

title = {Diagnostic Bayesian networks for diagnosing air handling units faults - part I: Faults in dampers, fans, filters and sensors},

author = {Yang Zhao and Jin Wen and Linda Xiao and Xuebin Yang and Shengwei Wang},

url = {https://doi.org/10.1016/j.applthermaleng.2015.09.121},

doi = {10.1016/j.applthermaleng.2015.09.121},

year  = {2016},

date = {2016-03-01},

journal = {Applied Thermal Engineering},

volume = {111},

abstract = {Faults in air handling units (AHUs) affect the building energy efficiency and indoor environmental quality significantly. There is still a lack of effective methods for diagnosing AHU faults automatically. In this study, a diagnostic Bayesian networks (DBNs)-based method is proposed to diagnose 28 faults, which cover most of common faults in AHUs. The basic idea is to fully utilize all diagnostic information in an information fusion way. The DBNs are developed based on a comprehensive survey of AHU fault detection and diagnosis (FDD) methods and fault patterns reported in three AHU FDD projects including NIST 6964, ASHRAE projects RP-1020 and RP-1312. The study is published in two parts. In the Part I, the methodology is described firstly. Four DBNs are developed to diagnose faults in fans, dampers, ducts, filters and sensors. There are 10 typical faults concerned and 14 fault detectors introduced. Evaluations are made using the experimental data from the ASHRAE Project RP-1312. Results show that the DBN-based method is effective in diagnosing faults even when the diagnostic information is uncertain and incomplete.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LANGEVIN2016372,

title = {Quantifying the human–building interaction: Considering the active, adaptive occupant in building performance simulation},

author = {Jared Langevin and Jin Wen and Patrick L. Gurian},

url = {https://www.sciencedirect.com/science/article/pii/S037877881530267X},

doi = {https://doi.org/10.1016/j.enbuild.2015.09.026},

issn = {0378-7788},

year  = {2016},

date = {2016-01-01},

journal = {Energy and Buildings},

volume = {117},

pages = {372-386},

abstract = {This paper introduces a Human and Building Interaction Toolkit (HABIT) for simulating the thermally adaptive behaviors and comfort of office occupants alongside building energy consumption. The toolkit uses the Building Controls Virtual Test Bed (BCVTB) to co-simulate a field-tested, agent-based behavior model with an EnergyPlus medium office model. The usefulness of the toolkit is demonstrated through a series of zone and building-level case study simulations that examine the wisdom of pairing local heating and cooling options with strategic thermostat set point offsets, judging from the energy, Indoor Environmental Quality (IEQ), and cost perspectives. Results generally suggest that trading efficient local heating/cooling options for whole space conditioning has both energy and comfort benefits, saving up to 28% of monthly HVAC energy while improving the acceptability of thermal conditions in a Philadelphia climate. Nevertheless, cost analysis shows that the fuel source of conserved energy must be considered – particularly in the case of personal heater use, which adds to electric plug loads and associated utility and CO2 emissions cost penalties. Moreover, costs from even small changes in simulated occupant productivity tend to overwhelm energy costs, suggesting the need to improve the accuracy and precision of available productivity models across multiple seasons and climates.},

keywords = {Agent-based modeling, Building performance modeling, Co-simulation, Occupant behavior, Thermal comfort},

pubstate = {published},

tppubtype = {article}

}

@article{LI201669,

title = {Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification},

author = {Xiwang Li and Jin Wen and Er-Wei Bai},

url = {https://www.sciencedirect.com/science/article/pii/S0306261915015688},

doi = {https://doi.org/10.1016/j.apenergy.2015.12.002},

issn = {0306-2619},

year  = {2016},

date = {2016-01-01},

journal = {Applied Energy},

volume = {164},

pages = {69-88},

abstract = {Optimal automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy demand, and to improve the power resilience. In order to achieve such automatic operation, high fidelity and computationally efficiency whole building energy forecasting models are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control. However, typical black box models often require long training period and are bounded to building operation conditions during the training period. On the other hand, creating a grey box model often requires (a) long calculation time due to parameter optimization process; and (b) expert knowledge during the model development process. This paper attempts to quantitatively evaluate the impacts of two significant system characteristics: system nonlinearity and response time, on the accuracy of the model developed by a system identification process. A general methodology for building energy forecasting model development is then developed. How to adapt the system identification process based on these two characteristics is also studied. A set of comparison criteria are then proposed to evaluate the energy forecasting models generated from the adapted system identification process against other methods reported in the literature, including Resistance and Capacitance method, Support Vector Regression method, Artificial Neural Networks method, and N4SID subspace algorithm. Two commercial buildings: a small and a medium commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and evaluation. The results from this study show that the adapted system identification process is capable of significantly improve the performance of the energy forecasting model, which is more accurate and more extendable under both of the noise-free and noisy conditions than those models generated by other methods.},

keywords = {Building energy modeling, Model based optimization, Monte Carlo simulation, System identification, System nonlinearity, System response time},

pubstate = {published},

tppubtype = {article}

}

@article{LI2016227,

title = {System identification and data fusion for on-line adaptive energy forecasting in virtual and real commercial buildings},

author = {Xiwang Li and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0378778816306922},

doi = {https://doi.org/10.1016/j.enbuild.2016.08.014},

issn = {0378-7788},

year  = {2016},

date = {2016-01-01},

journal = {Energy and Buildings},

volume = {129},

pages = {227-237},

abstract = {Accurate, computationally efficient, and cost-effective energy forecasting models are essential for model based control. Existing studies in model based control have mostly been focusing on developing energy forecasting models using simplified physics based or data driven models. However, creating and identification the simplified physics model are often challenging, which requires expert knowledge for model simplification and significant engineering efforts for model training. In addition, the accuracy and robustness of data driven models are always bounded by the training data. To this end, developing high fidelity energy forecasting models with less engineering effort and good performance is still an urgent task. Although the previous studies from the authors have shown great promises in a system identification model and outperformed other data-driven and grey box models, they still have large errors at the special operation situations. Therefore, this paper investigates a novel methodology to develop energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. The data fusion approach is able to adapt the forecasting model under the special operation situations based on the real measurements. An eigensystem realization algorithm based model reformation method is developed to convert the system identification models into state space models. Kalman filter based data fusion techniques are then implemented on the state space models to improve the model accuracy and robustness. The developed methodology are evaluated using data from a virtual building (simulated) and a real small size commercial building. Three different data fusion intervals: 15, 30, and 60min, have been tested. The overall building energy estimation accuracy from this proposed methodology can reach to above 95% in the virtual building and around 90% in the real building. The results also show that the shorter data fusion interval used, the higher accuracy can be achieved.},

keywords = {Building energy forecasting, Data fusion, On-line estimation, Real field implementation, System identification},

pubstate = {published},

tppubtype = {article}

}

@article{POURARIAN201653,

title = {Efficient and robust optimization for building energy simulation},

author = {Shokouh Pourarian and Anthony Kearsley and Jin Wen and Amanda Pertzborn},

url = {https://www.sciencedirect.com/science/article/pii/S0378778816302651},

doi = {https://doi.org/10.1016/j.enbuild.2016.04.019},

issn = {0378-7788},

year  = {2016},

date = {2016-01-01},

journal = {Energy and Buildings},

volume = {122},

pages = {53-62},

abstract = {Efficiently, robustly and accurately solving large sets of structured, non-linear algebraic and differential equations is one of the most computationally expensive steps in the dynamic simulation of building energy systems. Here, the efficiency, robustness and accuracy of two commonly employed solution methods are compared. The comparison is conducted using the HVACSIM+ software package, a component based building system simulation tool. The HVACSIM+ software presently employs Powell’s Hybrid method to solve systems of nonlinear algebraic equations that model the dynamics of energy states and interactions within buildings. It is shown here that the Powell’s method does not always converge to a solution. Since a myriad of other numerical methods are available, the question arises as to which method is most appropriate for building energy simulation. This paper finds considerable computational benefits result from replacing the Powell’s Hybrid method solver in HVACSIM+ with a solver more appropriate for the challenges particular to numerical simulations of buildings. Evidence is provided that a variant of the Levenberg-Marquardt solver has superior accuracy and robustness compared to the Powell’s Hybrid method presently used in HVACSIM+.},

keywords = {Building energy systems, Efficiency, HVAC simulation, Levenberg-Marquardt method, Numerical method, Powell’s Hybrid method, Robustness},

pubstate = {published},

tppubtype = {article}

}

@article{LI201698,

title = {An operation optimization and decision framework for a building cluster with distributed energy systems},

author = {Xiwang Li and Jin Wen and Ali Malkawi},

url = {https://www.sciencedirect.com/science/article/pii/S0306261916308054},

doi = {https://doi.org/10.1016/j.apenergy.2016.06.030},

issn = {0306-2619},

year  = {2016},

date = {2016-01-01},

journal = {Applied Energy},

volume = {178},

pages = {98-109},

abstract = {Driven by the development of smart buildings and smart grids, numerous of research has focused on developing optimal operation strategies for smart buildings with the aims of reducing energy consumption and cost, as well as improving the grid reliability. Unfortunately, most of the studies from smart building perspective only target on a single building with elaborated energy forecasting models. Few of them addresses the effects of multiple buildings on power grid operation. On the other hand, a few studies from smart grid area focus on multiple buildings and their influence on power grid, they usually, however, use simplified linear energy forecasting models, which are hard to guarantee the findings reflecting the cases in real fields. As a result, this research proposes to bridge this research gap, through developing and validating high fidelity energy forecasting models for a building cluster with multiple buildings and distributed energy systems, as well as creating a collaborative operation framework to determining the optimal operation strategies of this building cluster. The operation framework utilizes multi-objective optimizations to determine the operation strategies: building temperature setpoints, energy storage charging and discharging schedules, etc., using particle swarm optimization. Pareto curves for energy cost saving and thermal comfort maintaining are also derived with different thermal comfort requirements. The results from this study show that the developed building cluster collaborative operation framework is able to reduce the energy cost by 12.1–58.3% under different electricity pricing plans and thermal comfort requirements.},

keywords = {Demand response, Multi-objective optimization, Particle swarm optimization, Smart building, Smart grid},

pubstate = {published},

tppubtype = {article}

}

@article{LANGEVIN201594,

title = {Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices},

author = {Jared Langevin and Patrick L. Gurian and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0272494415000225},

doi = {https://doi.org/10.1016/j.jenvp.2015.01.007},

issn = {0272-4944},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Environmental Psychology},

volume = {42},

pages = {94-115},

abstract = {This paper presents findings from a one-year longitudinal case study of occupant thermal comfort and related behavioral adaptations in an air-conditioned office building. Long-term data were collected via online daily surveys and datalogger measurements of the local thermal environment and behavior. Behavioral outcomes are examined against both environmental and personal thermal comfort variables. Key personal variables include one's currently acceptable range of thermal sensations, which significantly explains inter-individual variations in thermal comfort responses. Results also show substantial between-day clothing adjustments and elevated metabolic rates upon office arrival, which may affect subsequent thermal comfort and behavior trajectories. Behavior sequencing appears complex, with multiple behaviors sometimes observed within a short time period and certain behaviors subject to contextual constraints. By elucidating the nature of the human-building interaction, the paper's findings may inform the improved measurement, modeling, and anticipation of occupant behavior as part of future sustainable building design and operation practices.},

keywords = {Longitudinal field studies, Occupant behavior, Office buildings, Thermal acceptability, Thermal comfort},

pubstate = {published},

tppubtype = {article}

}

@article{ZHAO2015145,

title = {Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors},

author = {Yang Zhao and Jin Wen and Shengwei Wang},

url = {https://www.sciencedirect.com/science/article/pii/S1359431115006584},

doi = {https://doi.org/10.1016/j.applthermaleng.2015.07.001},

issn = {1359-4311},

year  = {2015},

date = {2015-01-01},

journal = {Applied Thermal Engineering},

volume = {90},

pages = {145-157},

abstract = {This is the second part of a study on diagnostic Bayesian networks (DBNs)-based method for diagnosing faults in air handling units (AHUs) in buildings. In this part, 4 DBNs are developed to diagnose faults in heating/cooling coils, sensors and faults in secondary supply chilled water/heating water systems. There are 18 typical faults concerned and 35 fault detectors introduced. The DBNs are developed mainly on the basis of first principles and fault patterns resulted from literature and three AHU fault detection and diagnosis (FDD) projects. Efficient fault detection rules/methods from a comprehensive literature survey are integrated into the DBNs. Also, some new fault detection rules are developed. The 4 DBNs were evaluated using experimental data from ASHRAE Project RP-1312. Results show that the proposed DBNs effectively diagnosed AHU faults.},

keywords = {Air handling unit, Bayesian network, Fault detection, Fault diagnosis},

pubstate = {published},

tppubtype = {article}

}

@article{LANGEVIN201527,

title = {Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors},

author = {Jared Langevin and Jin Wen and Patrick L. Gurian},

url = {https://www.sciencedirect.com/science/article/pii/S0360132314004090},

doi = {https://doi.org/10.1016/j.buildenv.2014.11.037},

issn = {0360-1323},

year  = {2015},

date = {2015-01-01},

journal = {Building and Environment},

volume = {88},

pages = {27-45},

abstract = {This paper develops and validates an agent-based model (ABM) of occupant behavior using data from a one-year field study in a medium-sized, air-conditioned office building. The full ABM is presented in detail using a standard protocol for describing this type of model. Simulated occupant “agents” in the full ABM behave according to Perceptual Control Theory, taking the most immediate, unconstrained adaptive behaviors as needed to maintain their current thermal sensation within a reference range of seasonally acceptable sensations. ABM validation assigns simulated agents the personal characteristics and environmental context of real office occupants in the field study; executes the model; and compares the model's ability to predict observed fan, heater, and window use to the predictive abilities of several other behavior modeling options. The predictive performance of the full ABM compares favorably to that of the other modeling options on both the individual and aggregate outcome levels. The full ABM also appears capable of reproducing more familiar regression relationships between behavior and the local thermal environment. The paper concludes with a discussion of the model's current limitations and possibilities for future development.},

note = {Interactions between human and building environment},

keywords = {Agent-based modeling, Human-building interaction, Occupant behavior, Thermal acceptability, Thermal comfort},

pubstate = {published},

tppubtype = {article}

}

@article{Zhao2014-zs,

title = {A robust pattern recognition-based fault detection and diagnosis  (FDD) method for chillers},

author = {Yang Zhao and Fu Xiao and Jin Wen and Yuehong Lu and Shengwei Wang},

year  = {2014},

date = {2014-10-01},

urldate = {2014-10-01},

journal = {HVAC&R Research},

volume = {20},

number = {7},

pages = {798--809},

publisher = {Taylor & Francis},

abstract = {Ä new chiller fault detection and diagnosis (FDD) method is 

 proposed in this article. Different from conventional chiller 

 FDD methods, this article considers the FDD problem as a typical 

 one-class classification problem. The fault-free data are 

 classified as the fault-free class. Data of a fault type are 

 regarded as a fault class. The task of fault detection is to 

 detect whether the process data are outliers of the fault-free 

 class. The task of fault diagnosis is to find to which fault 

 class does the process data belong. In this study, support 

 vector data description (SVDD) algorithm is introduced for the 

 one-class classification. The basic idea of the SVDD-based 

 method is to find a minimum-volume hypersphere in a high 

 dimensional feature space to enclose most of the data of an 

 individual class. The proposed method is validated using the 

 ASHRAE RP-1043 (Comstock and Braun 1999) experimental data. It 

 shows more powerful FDD capacity than multi-class SVM-based FDD 

 methods and PCA-based fault detection methods. Four potential 

 applications of the proposed method are also discussed."},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2014-lm,

title = {Stability and accuracy of variable air volume box control at low 

 flows. Part 2: Controller test, system test, and field test},

author = {Ran Liu and Jin Wen and Xiaohui Zhou and Curtis Klaassen and Adam Regnier},

year  = {2014},

date = {2014-01-01},

journal = {HVAC&R Research},

volume = {20},

number = {1},

pages = {19--35},

publisher = {Taylor & Francis},

abstract = {This article with its companion paper (Liu et al. 2013), 

 summarizes the findings of ASHRAE Research Project 1353 

 (Stability and Accuracy of VAV Box Control at Low Flows). This 

 project aims to identify the major factors that cause the 

 airflow measurement in a variable air volume system to be 

 inaccurate and unstable, especially at low airflow conditions. 

 Both a laboratory test (including variable air volume sensor 

 test, controller test, and system test) and field test were 

 conducted; the companion work discussed the variable air volume 

 sensor test. In this article, findings from the controller test, 

 system test, and field test are summarized. The controller tests 

 involved testing of four controllers from four different 

 manufacturers. Testing was performed for accuracy, stability, 

 resolution, and ambient temperature effect. For the system test, 

 the variable air volume box and the controller were operated 

 together and tested as terminal unit systems. Two terminal units 

 were tested, and it was found that the performance of a variable 

 air volume terminal unit is highly dependent upon on controller 

 performance. Zeroing and balancing at a low airflow rate 560 fpm 

 (2.84 m/s) or 200 cfm (0.09 m3/s) for an 8-in. (0.2-m) box were 

 effective for achieving high system accuracy at low airflow 

 ranges. For the field tests, five variable air volume terminal 

 units were tested in real commercial buildings. It was found 

 that system balancing was not always an effective way to reduce 

 the variable air volume airflow sensor error in the field due to 

 the uncertainty of reference airflow measurement methods 

 commonly adopted in the field testing and balancing process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2014-ys,

title = {Stability and accuracy of variable air volume box control at low 

 flows. Part 1: Laboratory test setup and variable air volume 

 sensor test},

author = {Ran Liu and Jin Wen and Xiaohui Zhou and Curtis Klaassen},

year  = {2014},

date = {2014-01-01},

journal = {HVAC&R Research},

volume = {20},

abstract = {Variable air volume systems with direct digital controllers have 

 been widely adopted in the HVAC system of commercial, industrial, 

 and large residential buildings because they provide better 

 energy efficiency and occupant comfort. Normally, a variable air 

 volume terminal unit defines a minimum airflow rate to satisfy 

 the space ventilation requirement and/or the proper operation of 

 a terminal heating coil, if so equipped. However, it has been 

 found that variable air volume terminal units often fail to 

 perform as expected at the minimum airflow range (below 500 fpm 

 [2.5 m/s]). Under such a flow range, the embedded airflow sensor 

 becomes inaccurate, and the designed minimum airflow rate is less 

 than the minimum controllable airflow rate. This results in a 

 series of problems, including lack of ventilation, uneven airflow 

 control, reduced damper and operator life, and energy waste. 

 Through designed laboratory and field tests, this study (ASRHAE 

 Research Project RP-1353) aims to identify the major factors that 

 cause inaccuracy and instability issues in variable air volume 

 terminal units and the relationship between the major factors and 

 performance of the airflow sensor, controller, and terminal unit 

 system. Laboratory tests performed in this study included a 

 variable air volume sensor test, controller test, and system 

 test. Four variable air volume boxes from three manufacturers and 

 four controllers from four manufacturers were tested 

 systematically. Two identical test beds with high accuracy 

 ($pm$0.5%) reference airflow meters were designed and 

 constructed in the test facility. The size of the reference 

 airflow measuring stations was carefully selected to provide 

 maximum airflow measuring accuracy and maximum available system 

 pressure drop. This article describes the laboratory test setup 

 and summarizes the variable air volume sensor test results. A 

 companion article summarizes the controller test, system test, 

 and field test results. From the variable air volume sensor test, 

 three factors, namely, inlet conditions, low variable air volume 

 damper positions, and low airflow rates, are identified as 

 strongly impacting variable air volume terminal unit performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LI201463,

title = {A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform},

author = {Shun Li and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0378778813005410},

doi = {https://doi.org/10.1016/j.enbuild.2013.08.044},

issn = {0378-7788},

year  = {2014},

date = {2014-01-01},

journal = {Energy and Buildings},

volume = {68},

pages = {63-71},

abstract = {Building automation systems (BASs) are widely used in modern buildings and large amounts of data are available on the BAS central station. This abundance of data has been described as a data rich but information poor situation and has given an opportunity to better utilize the collected BAS data for fault detection and diagnostics (AFDD) purposes. Air-handling units (AHUs) operate in dynamic environment with changing weather conditions and internal loads. It is challenging for FDD method to distinguish differences caused by normal weather conditions change or by faults. Principle Component Analysis (PCA) has been found to be powerful as a data-driven model based method in detecting AHU faults. Wavelet transform is a promising data preprocess approach to solve the problem by removing the influence of weather condition change. A combined Wavelet-PCA method is developed and tested using site-data. The feasibility of using wavelet transform method for data pretreatment has been demonstrated in this study. Comparing to conventional PCA method, Wavelet-PCA method is more robust to the internal load change and weather impact and generate no false alarms.},

keywords = {AHU, BAS, Fault detection and diagnostics, HVAC, Principle Component Analysis, Wavelet transform},

pubstate = {published},

tppubtype = {article}

}

@article{LIU201481,

title = {Improving airflow measurement accuracy in VAV terminal units using flow conditioners},

author = {Ran Liu and Jin Wen and Michael S. Waring},

url = {https://www.sciencedirect.com/science/article/pii/S0360132313002795},

doi = {https://doi.org/10.1016/j.buildenv.2013.09.015},

issn = {0360-1323},

year  = {2014},

date = {2014-01-01},

journal = {Building and Environment},

volume = {71},

pages = {81-94},

abstract = {A variable air volume (VAV) terminal unit adjusts its supply airflow rate to meet the heating or cooling load and/or the ventilation requirement of the served space. Consequently, the accuracy of the VAV airflow sensor is highly important to the VAV system operation, and an inaccuracy of the VAV airflow sensor could lead to an energy waste or insufficient ventilation. ASHRAE Research Project (RP) 1353 identified non-ideal inlet conditions, such as an elbow or kinked duct before the VAV terminal unit, as causes of observed inaccuracies of up to 45% in VAV airflow measurements. VAV airflow measurement errors are normally mitigated by on-site balancing; however, it is difficult to achieve accurate reference airflow measurements in the field because of limited straight ductwork before VAV terminal units, as well as ductwork leakage. This study explored the potential solution of using a VAV flow conditioner to regulate the velocity profile upstream of the VAV airflow sensor and increase the VAV airflow measurement accuracy. A variety of flow conditioners were evaluated with computational fluid dynamics (CFD) modeling, and a CFD-optimized prototype of a 60%-porosity K-Lab/Laws plate was fabricated and tested. For all tested inlet conditions, airflow rates, and VAV boxes, the prototype reduced the VAV airflow reading error to ±5% when it was installed immediately before the VAV box inlet, regardless of upstream duct conditions. The prototype flow conditioner had a pressure drop equivalent to that of a 2-row VAV reheat coil.},

keywords = {Airflow measurement, Airflow reading error, Flow conditioner, Inlet condition, VAV},

pubstate = {published},

tppubtype = {article}

}

@article{XIAO2014106,

title = {Bayesian network based FDD strategy for variable air volume terminals},

author = {Fu Xiao and Yang Zhao and Jin Wen and Shengwei Wang},

url = {https://www.sciencedirect.com/science/article/pii/S0926580513001878},

doi = {https://doi.org/10.1016/j.autcon.2013.10.019},

issn = {0926-5805},

year  = {2014},

date = {2014-01-01},

journal = {Automation in Construction},

volume = {41},

pages = {106-118},

abstract = {This paper presents a diagnostic Bayesian network (DBN) for fault detection and diagnosis (FDD) of variable air volume (VAV) terminals. The structure of the DBN illustrates qualitatively the casual relationships between faults and symptoms. The parameters of the DBN describe quantitatively the probabilistic dependences between faults and evidence. The inputs of the DBN are the evidences which can be obtained from measurements in building management systems (BMSs) and manual tests. The outputs are the probabilities of faults concerned. Two rules are adopted to isolate the fault on the basis of the fault probabilities to improve the robustness of the method. Compared with conventional rule-based FDD methods, the proposed method can work well with uncertain and incomplete information, because the faults are reported with probabilities rather than in the Boolean format. Evaluations are made on a dynamic simulator of a VAV air-conditioning system serving an office space using TRNSYS. The results show that it can correctly diagnose ten typical VAV terminal faults.},

keywords = {Bayesian network, Fault detection, Fault diagnosis, VAV terminal},

pubstate = {published},

tppubtype = {article}

}

@article{LI201449,

title = {Application of pattern matching method for detecting faults in air handling unit system},

author = {Shun Li and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0926580514000545},

doi = {https://doi.org/10.1016/j.autcon.2014.03.002},

issn = {0926-5805},

year  = {2014},

date = {2014-01-01},

journal = {Automation in Construction},

volume = {43},

pages = {49-58},

abstract = {This paper presents a hybrid air handling unit (AHU) fault detection strategy based on Principal Component Analysis (PCA) method and Pattern Matching method. The basic idea of the pattern matching method is to locate periods of operation from a historical data set whose operational conditions are similar to the target operating condition. The proposed Pattern Matching-PCA method uses two similarity factors, PCA similarity factors and Distance similarity factors, to characterize the degree of similarity between historical data window and current snapshot data. PCA model is then built using the historical AHU operation dataset that are identified to be similar to current snapshot operation data. The method is validated by operational data of an AHU system in real building. The results show that the sensibility of PCA models is enhanced by preprocessing the training data with the Pattern Matching method.},

keywords = {AHU, BAS, Fault detection and diagnostics, HVAC, Pattern Matching, Principle Component Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{LI2014517,

title = {Review of building energy modeling for control and operation},

author = {Xiwang Li and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S1364032114003815},

doi = {https://doi.org/10.1016/j.rser.2014.05.056},

issn = {1364-0321},

year  = {2014},

date = {2014-01-01},

journal = {Renewable and Sustainable Energy Reviews},

volume = {37},

pages = {517-537},

abstract = {Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Better or even optimal building energy control and operation strategies provide great opportunities to reduce building energy consumption. Moreover, it is estimated by the National Energy Technology Laboratory that more than one-fourth of the 713GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. Energy forecasting models for building energy systems are essential to building energy control and operation. Three general categories of building energy forecasting models have been reported in the literature which include white-box (physics-based), black-box (data-driven), and gray-box (combination of physics based and data-driven) modeling approaches. This paper summarizes the existing efforts in this area as well as other critical areas related to building energy modeling, such as short-term weather forecasting. An up-to-date overview of research on application of building energy modeling methods in optimal control for single building and multiple buildings is also summarized in this paper. Different model-based and model-free optimization methods for building energy system operation are reviewed and compared in this paper. Agent based modeling, as a new modeling strategy, has made a remarkable progress in distributed energy systems control and optimization in the past years. The research literature on application of agent based model in building energy system control and operation is also identified and discussed in this paper.},

keywords = {Building energy modeling, Building optimal control, Demand response, Energy generation system, Energy storage system},

pubstate = {published},

tppubtype = {article}

}

@article{LI20141,

title = {Building energy consumption on-line forecasting using physics based system identification},

author = {Xiwang Li and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0378778814005581},

doi = {https://doi.org/10.1016/j.enbuild.2014.07.021},

issn = {0378-7788},

year  = {2014},

date = {2014-01-01},

journal = {Energy and Buildings},

volume = {82},

pages = {1-12},

abstract = {Model based control has become a promising solution for building operation optimization and energy saving. Accuracy and computationally efficiency are two of the most important requirements for building energy models. Existing studies in this area have mostly been focusing on reducing computation burden using simplified physics based modeling approach. However, creating even the simplified physics based model is often challenging and time consuming. Pure date-driven statistical models have also been adopted in a lot of studies. Such models, unfortunately, often require long training period and are bounded to building operating conditions that they are trained for. Therefore, this study proposes a novel methodology to develop building energy estimation models for on-line building control and optimization using a system identification approach. Frequency domain spectral density analysis is implemented in this on-line modeling approach to capture the dynamics of building energy system and forecast the energy consumption with more than 90% accuracy and less than 2min computational speed. A systematic analysis of system structure, system order and system excitation selection are also demonstrated. The forecasting results from this proposed model are validated against detailed physics based simulation results using a mid-size commercial building EnergyPlus model.},

keywords = {Building control and operation, Building energy efficiency, Building energy modeling, System identification},

pubstate = {published},

tppubtype = {article}

}

@article{LANGEVIN20131358,

title = {Reducing energy consumption in low income public housing: Interviewing residents about energy behaviors},

author = {Jared Langevin and Patrick L. Gurian and Jin Wen},

url = {https://www.sciencedirect.com/science/article/pii/S0306261912005144},

doi = {https://doi.org/10.1016/j.apenergy.2012.07.003},

issn = {0306-2619},

year  = {2013},

date = {2013-01-01},

journal = {Applied Energy},

volume = {102},

pages = {1358-1370},

abstract = {Low-income housing constitutes an important but often overlooked area for energy use reductions within the US residential sector. Given the scarcity of existing information on this subject, this study uses a semi-structured interview format to explore the key behavioral tendencies, energy knowledge gaps, and attitudes among low-income public housing residents, with the goal of demonstrating a process for developing, scoring, and analyzing the interviews that will be useful to other researchers when first engaging complex subjects like behavior in contexts that are not well covered by existing literature. Methods for sampling subjects and iteratively developing an interview guide and response-scoring framework are described, and the usefulness of this approach in allowing both quantitative and qualitative analysis of behavior response data is demonstrated. The paper concludes by illustrating how themes that emerge from the response analysis can be used to inform future surveying and intervention efforts. Key themes include the varying definitions for “comfort” amongst residents; the lack of resident control over the household environment; the tendency for residents to evaluate energy conservation measures (ECMs) in terms of costs, savings, and comfort; the muted differences in behavior between those who do and do not pay energy bills; and the importance of building maintenance and resident energy education to ongoing efficiency efforts.},

note = {Special Issue on Advances in sustainable biofuel production and use - XIX International Symposium on Alcohol Fuels - ISAF},

keywords = {Low income housing, Occupant behavior, Residential energy efficiency},

pubstate = {published},

tppubtype = {article}

}

@article{LANGEVIN2013206,

title = {Modeling thermal comfort holistically: Bayesian estimation of thermal sensation, acceptability, and preference distributions for office building occupants},

author = {Jared Langevin and Jin Wen and Patrick L. Gurian},

url = {https://www.sciencedirect.com/science/article/pii/S0360132313002151},

doi = {https://doi.org/10.1016/j.buildenv.2013.07.017},

issn = {0360-1323},

year  = {2013},

date = {2013-01-01},

journal = {Building and Environment},

volume = {69},

pages = {206-226},

abstract = {The three concepts of thermal sensation, acceptability, and preference each contribute to a holistic understanding of a building occupant's thermal comfort and how it can be effectively predicted. Nevertheless, there is currently no integrated framework for evaluating sensation, acceptability, and preference together as part of thermal comfort assessment in the built environment. Indeed, the only relation given between these variables in existing comfort guidelines - the Predicted Mean Vote – Predicted Percentage Dissatisfied (PMV–PPD) curve – rests on the tenuous assumption that occupants only find sensations at or near “Neutral” to be acceptable. This paper uses occupant response data from both the laboratory and field settings to develop an integrated approach for assessing office occupant thermal comfort through the multiple lenses of thermal sensation, acceptability, and preference. Specifically, probability distributions are developed for each of these comfort variables using Bayesian probit analysis. Given these distributions, we present revised PMV–PPD curves for field offices, and construct a new set of curves that represent the relationship between PMV and direct thermal acceptability and preference ratings. The probit analysis reveals that PMV is a significant predictor of thermal sensation distribution in the field; suggests that thermal acceptability and preference responses are subject to seasonal influences; and shows differences in thermal sensation, acceptability, and preference distributions for occupants in Air-Conditioned and Naturally Ventilated buildings. The usefulness of the developed distributions to practical thermal comfort assessments is discussed, as is the potential for these distributions to be updated in the future as more data are collected.},

keywords = {Bayesian probit analysis, Office occupants, Thermal acceptability, Thermal comfort, Thermal preference, Thermal sensation},

pubstate = {published},

tppubtype = {article}

}