期刊
BUILDINGS
卷 12, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/buildings12071052
关键词
building energy model; Bayesian calibration; sensitive analysis; automatic calibration method
资金
- UK Newton Fund
- Guangdong Department of Science and Technology OF FUNDER [101005-586174]
This paper introduces the application of Bayesian calibration (BC) method in building energy models, and presents a calibrated prediction model for office buildings in Guangdong, China. The model's accuracy meets the requirement of ASHRAE Guideline 14 and has significant implications for improving the quality and integrity of existing building energy databases.
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.
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