mlsanityData to 7 Oct 2026

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

arXiv 2105.13399 ↗First posted 27 May 2021cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 57 references that are in the map. First posted on arXiv 27 May 2021.

Builds on

9 of 57 references.

References · 9

  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  2. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  3. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  4. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  5. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  6. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  7. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  8. 2020Real-Time Model Calibration with Deep Reinforcement Learningcited by 12
  9. 2021Urban Building Energy Modeling (UBEM) Tools: A State-of-the-Art Review of bottom-up physics-based approachescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  2. 2025Multimodal Conditioned Diffusive Time Series Forecastingcited by 8
  3. 2024TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting modelcited by 6
  4. 2026Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanationscited by 0
  5. 2026Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecastingcited by 0