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.
Li, Xiwang; Wen, Jin
System identification and data fusion for on-line adaptive energy forecasting in virtual and real commercial buildings Journal Article
In: Energy and Buildings, vol. 129, pp. 227-237, 2016, ISSN: 0378-7788.
Abstract | Links | BibTeX | Tags: Building energy forecasting, Data fusion, On-line estimation, Real field implementation, System identification
@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}
}
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.
2014
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.