Research on Cooling Load Prediction of Subway Stations Based on Backpropagation Neural Network and Convolutional Neural Network
Cooling load prediction forms the foundation of building energy conservation.However,the variation in input variables significantly influences the predictive accuracy of neural networks.Furthermore,predicting loads for new buildings necessitates the accumulation of data.This study aims to determine the optimal inputs for predicting subway station cooling loads and evaluate the feasibility of database migration predictions.Leveraging empirical data and considering commonly used time variable,meteorological factors,and historical loads as potential inputs,this research compares the predictive accuracy of Backpropagation Neural Network(BPNN)and Convolutional Neural Network(CNN)under different inputs and during progressive database updates.Results indicate that the optimal input variables for cooling load prediction should exhibit Pearson correlation coefficient with the load greater than 0.5.Moreover,for similar building types,initial predictions can be made by gradually replacing the database,showcasing an enhancement in predictive accuracy with each update.Notably,the CNN demonstrate superior prediction performance throughout this process.
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