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Prediction of cooling effect of constant temperature community bin based on BP neural network

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Abstract

In order to explore the influence of outdoor microclimate on the cooling effect of constant temperature community bin, the temperature prediction model was predicted. The temperature and microclimate data sets of the community bin were collected in summer from May 2021 to September 2021. The climatic characteristics included cloudy and sunny conditions, and the environmental factors included outdoor temperature, air speed, air relative humidity, and solar radiation intensity. Stepwise regression method was used to test the significance of environmental factors, and the corresponding regression equation was obtained. BP neural network was used to establish temperature prediction models under cloudy and sunny conditions, respectively. The results showed that the coefficient of determination (R2) of the two models was above 0.8, and the environmental factors with significant influence were screened out. The root mean square error (RMSE) between the training value and the actual value established by BP neural network was 0.83 °C, and the determination coefficient (R2) was 0.968. Under sunny conditions, the root mean square error (RMSE) of predicted value and measured value was 0.65 °C, and the determination coefficient (R2) was 0.982. According to the analysis of the sample data, it showed that the BP neural network was more accurate than stepwise regression, and could be used to predict the temperature of community bin, which provided model basis for the practical application of intelligent temperature control community bin in summer.

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Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available due to privacy reasons, but are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Key R&D Program of China (grant number 2019YFC1906100).

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Authors and Affiliations

  1. College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China

    Xueru Zhu, Hua Li, Jialiang Xu, Jufei Wang, Samuel Mbugua Nyambura & Xuebin Feng

  2. Intelligent Agricultural Equipment Key Laboratory, College and Universities in Jiangsu Province, Nanjing Agricultural University, Nanjing, 210031, China

    Xueru Zhu, Jialiang Xu, Jufei Wang & Samuel Mbugua Nyambura

  3. Beijing Jinghuan Smart Environmental Protection Technology Co., Ltd, Beijing, 100020, China

    Wei Luo

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  1. Xueru Zhu
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  2. Hua Li
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  6. Xuebin Feng
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Correspondence to Hua Li.

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Zhu, X., Li, H., Xu, J. et al. Prediction of cooling effect of constant temperature community bin based on BP neural network. Int J Biometeorol 67, 587–596 (2023). https://doi.org/10.1007/s00484-023-02437-z

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  • DOI: https://doi.org/10.1007/s00484-023-02437-z

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