A meta-model-based optimization approach for fast and reliable calibration of building energy models. (1st December 2019)

• Record Type: Journal Article
• Authors: Chen, Jianli; Gao, Xinghua; Hu, Yuqing; Zeng, Zhaoyun; Liu, Yanan
• Is Part Of: Energy. Volume 188(2019)
• Publication Date: 2019-12-01
• Subjects: Building energy model calibration -- Meta-model -- Optimization -- Engineering judgment -- Gaussian process -- Emulator; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116046
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Building energy model calibration with optimization aims to bridge the gap between simulated energy consumption and measurement, thus aiding building retrofit and operation. However, the difficulty of the optimization in calibration including both optimization hyperparameter settings and problem complexity (multi-modal and under-determined) make the calibration with optimization approach difficult to be applied in practice with full reliability. Meanwhile, current calibration with optimization treats building calibration as a purely mathematical problem while neglecting the importance of engineering judgment in the calibration practice. In this paper, we introduced meta-models into the calibration with optimization approach with an auto-correction mechanism to improve calibration performance with respect to time and reliability. To better illustrate the approach, we presented a case study with validation. The proposed method was demonstrated to alleviate difficulty of optimization while improving calibration time and reliability in the study. Comparing two types of meta-models, we found that using the GP (Gaussian Process) achieved better performance with less computation time and higher accuracy compared to the MLR (Multiple Linear Regression). To efficiently train emulators, we can start with generating only a small amount of white-box simulation results. It is also important to generate enough initial starts to ensure robustness of calibration. Highlights: The study introduces the meta-model to aid building calibration with optimization. . Gaussian process outperforms Multiple Linear Regression as the meta-model. . A case study is presented to show the proposed approach performance. . The new approach helps overcome optimization complexity and hyperparameter setting. . The involvement of engineering judgement and recalibration is helpful .