Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network

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.

© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

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Work Title Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
Access
Open Access
Creators
  1. Yuqing Hu
  2. Xiaoyuan Cheng
  3. Suhang Wang
  4. Jianli Chen
  5. Tianxiang Zhao
  6. Enyan Dai
Keyword
  1. Building interdependency
  2. Urban-scale building simulation
  3. Graph neural network
  4. Time-series prediction
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
Publisher
  1. Elsevier BV
Publication Date February 2022
Publisher Identifier (DOI)
  1. 10.1016/j.apenergy.2021.118231
Source
  1. Applied Energy
Deposited June 17, 2022

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Version 1
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  • Created
  • Added manuscript_8_30-1.pdf
  • Added Creator Yuqing Hu
  • Added Creator Xiaoyuan Cheng
  • Added Creator Suhang Wang
  • Added Creator Jianli Chen
  • Added Creator Tianxiang Zhao
  • Added Creator Enyan Dai
  • Published
  • Updated Work Title, Keyword Show Changes
    Work Title
    • Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
    • ! Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
    Keyword
    • Building interdependency, Urban-scale building simulation, Graph neural network, Time-series prediction
  • Updated Work Title Show Changes
    Work Title
    • ! Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
    • Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
  • Updated