Model based control has been proven to have significant building energy saving potentials through operation optimization. Accurate and computationally efficient, and cost-effective building energy model are essential for model based control. Existing studies in this area have mostly been focusing on reducing computation burden using simplified physics based modeling approach. However, creating and identification the simplified physics based model is often challenging and requires significant engineering efforts. Therefore, this study proposes a novel methodology to develop building energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. System identification model has been developed based on frequency domain spectral density analysis. Eigensystem realization algorithm is used to generate the state space model from the Markov parameters. Kalman filter based data fusion technique has also been implemented to improve the accuracy and robustness of the model by incorporating with real measurements. A systematic analysis of system structure, system excitation selection as well as data fusion implementation is also demonstrated. The developed strategies are evaluated using a simulated testing building (simulated in EnergyPlus environment). The overall building energy estimation accuracy from this proposed model can reach to above 95% within 2 minutes calculation time, when compared against detailed physics based simulation results from the EnergyPlus model.
- Dynamic Systems and Control Division
Building Energy Consumption On-Line Forecasting Using System Identification and Data Fusion
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Li, X, & Wen, J. "Building Energy Consumption On-Line Forecasting Using System Identification and Data Fusion." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems. San Antonio, Texas, USA. October 22–24, 2014. V001T07A002. ASME. https://doi.org/10.1115/DSCC2014-6134
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