Research on building energy consumption prediction based on multi-level attention mechanism

Lu-han Qiao; Jing Yang; Ru-ping Shao; Jia-huan Mao

doi:10.1117/12.2645780

23 August 2022 Research on building energy consumption prediction based on multi-level attention mechanism

Lu-han Qiao, Jing Yang, Ru-ping Shao, Jia-huan Mao

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123051G (2022) https://doi.org/10.1117/12.2645780
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

According to the multi-dimensional and multi feature characteristics of building energy consumption data, considering the fluctuation of long-time series prediction, partial attention mechanism, full convolution neural network and full-time attention mechanism are introduced to capture the potential correlation between features and targets and the weight between features, and an improved GeoMAN (Geo-sensory Multi-level Attention Networks) building energy consumption prediction model is proposed, Realize the prediction of building energy consumption data. The improved GeoMAN model is used to compare the prediction indexes with four groups of common models, and other data sets are input to verify the generalization ability of the model. The experimental results show that the model performs better in RMSE, Mae and MRE, reflecting better predictability and robustness.

Citation Download Citation

Lu-han Qiao, Jing Yang, Ru-ping Shao, and Jia-huan Mao "Research on building energy consumption prediction based on multi-level attention mechanism", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123051G (23 August 2022); https://doi.org/10.1117/12.2645780

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