Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Cui, Yu; Zhu, Zishang; Zhao, Xudong; Li, Zhaomeng; Qin, Peng

Abstract

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability.

Citation

Cui, Y., Zhu, Z., Zhao, X., Li, Z., & Qin, P. (2022). Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model. Buildings, 12(7), Article 1052. https://doi.org/10.3390/buildings12071052

• Journal Article Type: Article
• Acceptance Date: Jul 15, 2022
• Online Publication Date: Jul 20, 2022
• Publication Date: Jul 1, 2022
• Deposit Date: Mar 19, 2024
• Publicly Available Date: Mar 20, 2024
• Journal: Buildings
• Electronic ISSN: 2075-5309
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 7
• Article Number: 1052
• DOI: https://doi.org/10.3390/buildings12071052
• Keywords: Building energy model; Bayesian calibration; Sensitive analysis; Automatic calibration method
• Public URL: https://hull-repository.worktribe.com/output/4079794