Load forecasting method of nearly zero energy buildings based on EMD and machine learning algorithm

The Pearson correlation coefficient method is used to analyse the correlation between different feature variables and cooling and heating loads,and the input feature variables of the prediction model are determined.The empirical mode decomposition (EMD) is used to decompose the daily cooling and heating loads according to frequency,and the machine learning algorithms,namely,back propagation neural network (BPNN),random forest (RF) and support vector machine (SVM),are used to train and verify the loads at different frequencies.Finally the predicted load of nearly zero energy buildings is obtained by reconstruction.Based on the above methods,this paper takes a nearly zero energy residential building in Beijing as the research object,and compares the accuracy of the prediction results of different algorithms.The results show that the method combining EMD with RF algorithm has a high accuracy in predicting the cooling and heating loads of nearly zero energy buildings.Furthermore,the exhaustive search method is used to optimize the preliminary parameters of the model,and the accuracy of the cooling and heating load forecasting results is improved.The determination coefficient (R2) and the average absolute percentage error (MAPE) of the cooling load forecasting results are 0.996 and 1.32%,respectively,and the R2and MAPE of the heating load forecasting results are 0.997 and 0.79%,respectively.