Probabilistic HVAC Load Forecasting Method Based on Transformer Network Considering Multiscale and Multivariable Correlation

Tingzhe Pan (), Zean Zhu, Hongxuan Luo, Chao Li, Xin Jin, Zijie Meng and Xinlei Cai
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Tingzhe Pan: Southern Power Grid Research Institute Co., Ltd., Guangzhou 510663, China
Zean Zhu: Power Dispatch Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Hongxuan Luo: Southern Power Grid Research Institute Co., Ltd., Guangzhou 510663, China
Chao Li: Power Dispatch Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Xin Jin: Southern Power Grid Research Institute Co., Ltd., Guangzhou 510663, China
Zijie Meng: Power Dispatch Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Xinlei Cai: Power Dispatch Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China

Energies, 2025, vol. 18, issue 19, 1-21

Abstract: Accurate load forecasting for community-level heating, ventilation, and air conditioning (HVAC) plays an important role in determining an efficient strategy for demand response (DR) and the operation of the power grid. However, community-level HVAC includes various building-level HVACs, whose usage patterns and standard parameters vary, causing the challenge of load forecasting. To this end, a novel deep learning model, multiscale and cross-variable transformer (MSCVFormer), is proposed to achieve accurate community-level HVAC probabilistic load forecasting by capturing the various influences of multivariables on the load pattern, providing effective information for the grid operators to develop DR and operation strategies. This approach is combined with the multiscale attention (MSA) and cross-variable attention (CVA) mechanism, capturing the complex temporal patterns of the aggregated load. Specifically, by embedding the time series decomposition into the self-attention mechanism, MSA enables the model to capture the critical features of time series while considering the correlation between multiscale time series. Then, CVA calculates the correlations between the exogenous variable and aggregated load, explicitly utilizing the exogenous variables to enhance the model’s understanding of the temporal pattern. This differs from the usual methods, which do not fully consider the relationship between the exogenous variable and aggregated load. To test the effectiveness of the proposed method, two datasets from Germany and China are used to conduct the experiment. Compared to the benchmarks, the proposed method achieves outperforming probabilistic load forecasting results, where the prediction interval coverage probability (PICP) deviation with the nominal coverage and prediction interval normalized averaged width (PINAW) are reduced by 46.7% and 5.25%, respectively.

Keywords: heating, ventilation, and air conditioning; deep learning; multi-horizon probabilistic forecasting; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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