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TSPM: Two-stage Spatiotemporal Prediction Module with Floor Plan for Effective Building Energy Prediction

TSPM: 효과적인 건물 에너지 예측을 위한 건물 안내도 기반 2단계 시공간 예측 모듈

  • Hyeonseok Jin (Dept. of Artificial Intelligent Convergence, Chonnam National University) ;
  • Kyungbaek Kim (Dept. of Artificial Intelligent Convergence, Chonnam National University)
  • 진현석 (전남대학교 인공지능융합학과) ;
  • 김경백 (전남대학교 인공지능융합학과)
  • Published : 2025.05.29

Abstract

Due to the increasing consumption in energy, accurate building energy prediction has become important, and recent studies achieve significant advancement with deep learning-based approaches. However, there are still some challenges. For accurate prediction, a complex structure that considers both temporal and spatial features must be applied, which makes it difficult to extend to other cases. Furthermore, some recent studies use simple time-series model as comparison, which may undermine the advantage of incorporating spatial features. In this paper, we propose novel Two-stage Spatiotemporal Prediction Module (TSPM) to overcome these limitations. Our TSPM can incorporate spatial features based on building floor plans images which can easily obtained. In addition, we conduct fair comparisons with promising time-series models to demonstrate effectiveness of our approach.

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Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629, 50%) grant funded by the Korea government(MSIT). This work was supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156287, 50%).