[1]GUO Maozu,YU Fengning,WANG Pengyue,et al.Building energy consumption prediction method based on temporal correlation[J].CAAI Transactions on Intelligent Systems,2026,21(1):214-225.[doi:10.11992/tis.202503013]
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Building energy consumption prediction method based on temporal correlation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 214-225 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2026-03-05
Title:
Building energy consumption prediction method based on temporal correlation
Author(s):
GUO Maozu1; 2;  YU Fengning1; 2;  WANG Pengyue1; 2;  LIU Xiaolong1; 2
1. School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China;
2. Beijing Key Laboratory of Super Intelligent Technology for Urban Architecture, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Keywords:
building energy consumption prediction; temporal correlation mining; building morphology features; self-supervised learning; contrastive loss function; encoder-decoder architecture; long-term dependency; temporal convolutional network
CLC:
TP3-05
DOI:
10.11992/tis.202503013
Abstract:
Building energy consumption prediction is crucial for optimizing energy resource allocation, advancing energy-saving and emission-reduction measures, and supporting sustainable development goals. Due to the influence of factors such as seasonal changes and holiday effects, building energy consumption data exhibits periodic and non-stationary characteristics in time series. Existing methods typically use sliding windows to model local temporal features, but they can only capture changes within the window and are limited in uncovering the long-term evolution trends across windows. In addition, architectural morphology significantly impacts energy consumption; however, it is often neglected in energy consumption prediction tasks. To address these limitations, this paper innovatively proposes a temporal correlation-based building energy consumption prediction method, which primarily includes local feature learning, global feature learning, and loss function design. To capture long-term changes beyond the window, the global feature learning module adopts an encoder-decoder architecture to model long-term temporal dependencies between sliding windows. Furthermore, a self-supervised contrastive loss function is designed, constructing positive and negative sample pairs at the window level to further explore the global correlations of energy consumption data. To address the neglect of architectural morphology features, building morphological features are embedded, and a linear layer is used to capture the local correlations of temporally adjacent energy consumption data within the sliding window. Experimental results show that the proposed method achieves the best prediction accuracy in multi-building energy consumption forecasting tasks, and in 24-hour ahead prediction tasks, the method improves prediction accuracy by approximately 17.06%, 8.37%, 9.79%, 9.58%, 9.83%, 6.94%, and 5.55% compared to commonly used energy consumption prediction methods including ARIMA, LSTM, GRU, Transformer, GWO-SARIMA-LSTM, Informer, and Autoformer. This method provides scientific support for building energy-saving management and energy usage behavior optimization.
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