Research on Building Load Forecasting Based on ARIMA-CNN-GRU Neural Network and Bayesian Optimization
Building energy consumption prediction is essential for energy consumption reduction and function rationalization.To solve the complex and changeable problems of building energy consumption,ARIMA model is used to solve the nonlinear part of the energy consumption curve,then CNN-GRU depth learning model is used to fit the nonlinear residuals,and Bayesian optimization algorithm is adopted for hyperparametric optimization.The prediction results based on load curves of different buildings prove that the Bayesian opti-mization algorithm can improve the accuracy of the model by more than 3 times,the maximum error of ARIMA-CNN-GRU algorithm pro-posed for different types of building load curve prediction is controlled within 7% .The accuracy value is 2 times higher than that of building load curve direct predicted through the CNN-GRU network,meeting the prediction of different building loads.
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