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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| Published in: | Applied energy Vol. 307; p. 118231 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.02.2022
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| ISSN: | 0306-2619, 1872-9118 |
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| Abstract | •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. |
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| AbstractList | •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. 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. |
| ArticleNumber | 118231 |
| Author | Hu, Yuqing Cheng, Xiaoyuan Chen, Jianli Zhao, Tianxiang Dai, Enyan Wang, Suhang |
| Author_xml | – sequence: 1 givenname: Yuqing surname: Hu fullname: Hu, Yuqing organization: Department of Architectural Engineering, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 2 givenname: Xiaoyuan surname: Cheng fullname: Cheng, Xiaoyuan organization: Department of Architectural Engineering, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 3 givenname: Suhang surname: Wang fullname: Wang, Suhang organization: College of Information Science and Technology, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 4 givenname: Jianli surname: Chen fullname: Chen, Jianli email: jianli.chen@utah.edu organization: Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA – sequence: 5 givenname: Tianxiang surname: Zhao fullname: Zhao, Tianxiang organization: College of Information Science and Technology, The Pennsylvania State University, University Park, PA 16802, USA – sequence: 6 givenname: Enyan surname: Dai fullname: Dai, Enyan organization: College of Information Science and Technology, The Pennsylvania State University, University Park, PA 16802, USA |
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