Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
This work addresses the need for more accurate urban building energy models to support energy-saving strategies in cities, which is crucial for improving urban sustainability, though it appears incremental by integrating existing methods with domain-specific knowledge.
The study tackled the problem of accurately modeling urban building energy consumption by capturing inter-building interdependencies, proposing a data-driven model using a spatial-temporal graph convolutional network (ST-GCN) that outperformed other time-series machine learning models in predicting hourly energy consumption on a university campus.
The world is increasingly urbanizing and the building industry accounts for more than 40% of energy consumption in the United States. 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 limited in their abilities to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those 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 UBEM synthesizing the solar-based building interdependency and spatial-temporal graph convolutional network (ST-GCN) algorithm. Especially, we took a university campus located in downtown Atlanta as an example to predict the hourly energy consumption. Furthermore, we tested the feasibility of the proposed 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 all others. In addition, the physical knowledge embedded in the model is well interpreted. After discussion, it is found that data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.