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

Yuqing Hu, Xiaoyuan Cheng, Suhang Wang, Jianli Chen, Tianxiang Zhao and Enyan Dai

Applied Energy, 2022, vol. 307, issue C, No S0306261921014963

Abstract: 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.

Keywords: Building interdependency; Urban-scale building simulation; Graph neural network; Time-series prediction (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.apenergy.2021.118231

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