MULTI-LOAD PREDICTION OF INTEGRATED ENERGY SYSTEM BASED ON DUAL FEATURE IDENTIFICATION

Li Jinhui; Wang Cong; Zhang Hongli; Ma Ping; Lu Xuehui

doi:10.19912/j.0254-0096.tynxb.2024-2354

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 58-69. DOI: 10.19912/j.0254-0096.tynxb.2024-2354

MULTI-LOAD PREDICTION OF INTEGRATED ENERGY SYSTEM BASED ON DUAL FEATURE IDENTIFICATION

Li Jinhui¹, Wang Cong², Zhang Hongli², Ma Ping², Lu Xuehui³

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Abstract

This paper proposes a method in order to effectively extract the relevant features of the data in the integrated energy system（IES）,identify the important features in the multi-load forecasting task,and improve the prediction accuracy and speed of multi-load forecasting. In this paper,a short-term prediction model of multiple loads in an integrated energy system based on an improved convolutional neural network（CNN） and an attention mechanism is proposed,which realizes dual feature recognition and accurate prediction of multiple loads. Firstly,the correlation analysis of the data is carried out to screen out the meteorological characteristics with strong correlation with multiple loads,and then improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN） is carried out on the electrical, cooling and heating load data, and the complex multi-load time series is efficiently decomposed into K intrinsic modes （IMFs）, and the correlation analysis of IMFs is carried out to screen out the key IMFs. Secondly,CNN is used for feature recognition,wherelocal important features of the data are extracted,and then the gated recurrent unit （GRU） is used to capture the dynamic changes of the time series data. Then,the attention mechanism is introduced to realize the dual feature recognition,enhance the attention of the model to the key features,and improve the accuracy of prediction. Finally,the multivariate load data of Arizona State University Tempe Campus were used for experimental testing,and comparative models were used for comparative analysis. The results show that the average absolute percentage errors of electrical,cooling and heating loads of the proposed method are 5.1%,4.23% and 3.68%,respectively,which has higher prediction accuracy and faster computational speed than other models.

Key words

integrated energy systems / load forecasting / modal decomposition / convolutional neural networks / gated recurrent unit / attention mechanism / feature extraction

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Li Jinhui, Wang Cong, Zhang Hongli, Ma Ping, Lu Xuehui. MULTI-LOAD PREDICTION OF INTEGRATED ENERGY SYSTEM BASED ON DUAL FEATURE IDENTIFICATION[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 58-69 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2354

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