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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Veröffentlicht in:	Applied energy Jg. 307; S. 118231
Hauptverfasser:	Hu, Yuqing, Cheng, Xiaoyuan, Wang, Suhang, Chen, Jianli, Zhao, Tianxiang, Dai, Enyan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2022
Schlagworte:	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
Online-Zugang:	Volltext
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