Zhu Xueru, Li Hua, Xu Jialiang, Wang Jufei, Nyambura Samuel Mbugua, Feng Xuebin, Luo Wei
College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
Intelligent Agricultural Equipment Key Laboratory, College and Universities in Jiangsu Province, Nanjing Agricultural University, Nanjing, 210031, China.
Int J Biometeorol. 2023 Apr;67(4):587-596. doi: 10.1007/s00484-023-02437-z. Epub 2023 Feb 7.
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 (R) 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 (R) 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 (R) 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.
为探究室外微气候对恒温社区垃圾桶降温效果的影响，对温度预测模型进行了预测。于2021年5月至2021年9月夏季收集了社区垃圾桶的温度和微气候数据集。气候特征包括阴天和晴天条件，环境因素包括室外温度、风速、空气相对湿度和太阳辐射强度。采用逐步回归法检验环境因素的显著性，并得到相应的回归方程。分别利用BP神经网络建立了阴天和晴天条件下的温度预测模型。结果表明，两个模型的决定系数（R）均高于0.8，且筛选出了影响显著的环境因素。BP神经网络建立的训练值与实际值之间的均方根误差（RMSE）为0.83℃，决定系数（R）为0.968。晴天条件下，预测值与测量值的均方根误差（RMSE）为0.65℃，决定系数（R）为0.982。根据样本数据分析表明，BP神经网络比逐步回归更准确，可用于预测社区垃圾桶温度，为智能控温社区垃圾桶夏季实际应用提供了模型依据。
