ENERGY CONSUMPTION PREDICTION MODEL FOR HEATING VENTILATION AIR CONDITIONING BASED ON TRANSFORMER MULTI-SCALE FUSION NETWORK

Yu Shui; Han Fuhong; Luo Yuchen; Sun Shengkun

doi:10.19912/j.0254-0096.tynxb.2024-1746

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 300-309. DOI: 10.19912/j.0254-0096.tynxb.2024-1746

ENERGY CONSUMPTION PREDICTION MODEL FOR HEATING VENTILATION AIR CONDITIONING BASED ON TRANSFORMER MULTI-SCALE FUSION NETWORK

Yu Shui, Han Fuhong, Luo Yuchen, Sun Shengkun

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Abstract

As the proportion of heating venlt lation air conditioning（HVAC） energy consumption in the overall energy consumption of buildings continues to increase, improving energy consumption prediction capability is an important technical measure for enhancing the fine management of building energy consumption and achieving carbon neutrality in the building sector. This paper proposes a Transformer-based multi-scale fusion network model for predicting HVAC energy consumption in buildings. By introducing a multi-scale pyramid module and a temporal convolutional network structure, the model effectively captures both local and global temporal features, thereby improving prediction accuracy. Experimental results show that the model outperforms traditional single models in prediction performance, with a significant reduction in root mean square error （RMSE） and mean absolute error （MAE）, and a determination coefficient （R²） of 0.9826. This model provides an efficient and accurate prediction tool for building energy consumption management and contributes to more efficient building energy management and energy-saving strategies.

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HVAC / feature extraction / deep learning / load forecasting / multi-scale features / Transformer model

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Yu Shui, Han Fuhong, Luo Yuchen, Sun Shengkun. ENERGY CONSUMPTION PREDICTION MODEL FOR HEATING VENTILATION AIR CONDITIONING BASED ON TRANSFORMER MULTI-SCALE FUSION NETWORK[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 300-309 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1746

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