Times series forecasting for urban building energy consumption based on graph convolutional network

•Develop a Spatiotemporal graph convolutional network for hourly energy predictions.•Inter-building impacts are considered in graph-based method for prediction.•Test ST-GCN on campus buildings and validate its improved performance.•Discuss the interpretability of the ST-GCN modelling results. The wo...

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Vydáno v:	Applied energy Ročník 307; s. 118231
Hlavní autoři:	Hu, Yuqing, Cheng, Xiaoyuan, Wang, Suhang, Chen, Jianli, Zhao, Tianxiang, Dai, Enyan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.02.2022
Témata:	algorithms Building interdependency energy conservation energy efficiency Graph neural network prediction summer time series analysis Time-series prediction Urban-scale building simulation winter Time-series prediction Building interdependency Graph neural network Urban-scale building simulation
ISSN:	0306-2619, 1872-9118
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Shrnutí:	•Develop a Spatiotemporal graph convolutional network for hourly energy predictions.•Inter-building impacts are considered in graph-based method for prediction.•Test ST-GCN on campus buildings and validate its improved performance.•Discuss the interpretability of the ST-GCN modelling results. The world is increasingly urbanizing, and to improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing buildings and constructing new communities. In this situation, an accurate urban building energy model (UBEM) is the foundation to support the design of energy-efficient communities. However, current UBEM are ineffective to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those conventional models either ignored or oversimplified these building interdependencies, which can substantially affect the accuracy of urban energy modeling. To fill the research gap, this study proposes a novel data-driven UBEN synthesizing the solar-based building interdependency and spatio-temporal graph convolutional network (ST-GCN) algorithm. Especially, we took a university campus located in the downtown area of Atlanta as an example to predict the hourly energy consumption. Furthermore, we tested the feasibility of the ST-GCN model by comparing the performance of the ST-GCN model with other common time-series machine learning models. The results indicate that the ST-GCN model overall outperforms in different scenarios, the mean absolute percentage error of ST-GCN is around 5%. More importantly, the accuracy of ST-GCN is enhanced when simulating buildings with higher edge weight and in-degrees, this phenomenon is magnified in summer daytime and winter daytime, which validated the interpretability of the ST-GCN models. After discussion, it is found that data-driven models integrated with engineering or physics knowledge can significantly improve urban building energy use prediction.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0306-2619 1872-9118
DOI:	10.1016/j.apenergy.2021.118231