RESEARCH ON SOLAR HEATING LOAD FORECASTING BASED ON CNN AND BILSTM NEURAL NETWORK MODEL

Zhou Zekai; Hou Hongjuan; Sun Li; Jin Tao

doi:10.19912/j.0254-0096.tynxb.2023-0962

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (10) : 415-422. DOI: 10.19912/j.0254-0096.tynxb.2023-0962

RESEARCH ON SOLAR HEATING LOAD FORECASTING BASED ON CNN AND BILSTM NEURAL NETWORK MODEL

Zhou Zekai, Hou Hongjuan, Sun Li, Jin Tao

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Abstract

Aiming at the phenomenon of energy waste caused by the mismatch between heat supply and demand in solar heating system, a short-term heat load forecasting model based on convolutional neural network-bidirectional long short-term memory neural network is proposed. Firstly, the data is cleaned to make the data accurate and complete. Secondly, the input features are screened according to the Pearson correlation coefficient. Finally, a convolutional neural network-bidirectional long-term and short-term memory neural network model is established based on its spatial-temporal characteristics. After detailed comparison and analysis with the single neural network model, the length of the memory neural network and the two-way long short-term memory neural network, the results show that the model has a significant improvement in accuracy compared with the traditional neural network model, which verifies the effectiveness of the model in the prediction of solar heating load.

Key words

solar heating / convolutional neural network（CNN） / long short-term memory（LSTM） / thermal load / neural network model

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Zhou Zekai, Hou Hongjuan, Sun Li, Jin Tao. RESEARCH ON SOLAR HEATING LOAD FORECASTING BASED ON CNN AND BILSTM NEURAL NETWORK MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 415-422 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0962

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