文章基本信息

标题：A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption
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作者：Oh, Kyoungcheol ; Kim, Eui-Jong ; Park, Chang-Young 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：15
页码：1-14
DOI：10.3390/su14159464
语种：English
出版社：MDPI, Open Access Journal
摘要：Predicting building energy consumption needs to be anticipated to save building energy and effectively control the predictions. This study depicted the target building as a physical model to improve the learning performance in a data-scarce environment and proposed a model that uses simulation results as the input for a data-driven model. Case studies were conducted with different quantities of data. The proposed hybrid method proposed in this study showed a higher prediction accuracy showing a cvRMSE of 22.8% and an MAE of 6.1% than using the conventional data-driven method and satisfying the tolerance criteria of ASHRAE Guideline 14 in all the test cases.
关键词：heat pump energy consumption prediction; physical modeling; data-driven model; data scarcity