An automated process to calibrate building energy model based on schedule tuning and signed directed graph method. (March 2021)

• Record Type: Journal Article
• Authors: Lyu, Yan; Pan, Yiqun; Yang, Tao; Li, Yuming; Huang, Zhizhong; Kosonen, Risto
• Is Part Of: Journal of building engineering. Volume 35(2021)
• Publication Date: 2021-03
• Subjects: Building simulation -- Model calibration -- HVAC System -- Schedule -- Signed directed graph (SDG); Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2020.102058
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: The calibration of building energy model is a vital part of the whole modelling process. To improve the efficiency of this work, an automation procedure has recently been introduced to the calibration process, but no generic approach has yet received the consensus of the whole community at present. The main reason is that a purely mathematics-based, automated calibration lacks physical explanation, which means that the calibrated model probably has a large error in certain single physical values despite a good overall agreement with the measurement data. In this study, the authors design a set of procedures to automatize the calibration process of building energy model based on schedule tuning and signed directed graph (SDG) method, which codifies human experience and logic and incorporates them into the modules of computational calibration to combine the advantages of traditional and automated approach. The specific operations of calibration process are introduced through a case study. In this case, a building energy model with relatively low accuracy is finally well calibrated. The CV(RMSE) (Coefficient of Variation of Root Mean Square Error) of the original model is 42.12% for power consumption and 25.50% for gas consumption; and for the calibrated model, the CV(RMSE) is 2.21% for power consumption and 3.15% for gas consumption. In addition, the same operations are also applied to another case for further verification. In this case, the final CV(RMSE) of power consumption is reduced to 2.19% from 19.25%. This significant result reveals the applicability and effectiveness of the automated process. Highlights: A new automated process to calibrate building energy model. Schedule tuning based on short-term monitoring data. Signed directed graph method to codify human experience of model calibration. Two case studies to verify the effectiveness of automated calibration.