Publications
2016
Li, Xiwang; Wen, Jin; Bai, Er-Wei
Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification Journal Article
In: Applied Energy, vol. 164, pp. 69-88, 2016, ISSN: 0306-2619.
Abstract | Links | BibTeX | Tags: Building energy modeling, Model based optimization, Monte Carlo simulation, System identification, System nonlinearity, System response time
@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}
}
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.
2014
Li, Xiwang; Wen, Jin
Review of building energy modeling for control and operation Journal Article
In: Renewable and Sustainable Energy Reviews, vol. 37, pp. 517-537, 2014, ISSN: 1364-0321.
Abstract | Links | BibTeX | Tags: Building energy modeling, Building optimal control, Demand response, Energy generation system, Energy storage system
@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}
}
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.
Li, Xiwang; Wen, Jin
Building energy consumption on-line forecasting using physics based system identification Journal Article
In: Energy and Buildings, vol. 82, pp. 1-12, 2014, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: Building control and operation, Building energy efficiency, Building energy modeling, System identification
@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}
}
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.