Research on artificial neural network and radial basis network model to predict building heating and cooling load
Artificial neural network(ANN)and radial basis function network(RBF)were used to predict the cooling and heat-ing loads of buildings,and significant factors affecting building energy consumption were identified.By optimizing the neuron num-ber in the hidden layer of ANN and RBF models,it was found that the ANN model with configurations of 8-65-1 and 8-97-1 could accurately predicted building heating and cooling loads which were consistent with the results in the dataset,with correlation coefficients(R2)of 0.962 and 0.953,respectively.The predicted results of the RBF models with configurations of 8-28-1 and 8-6-1 were more consistent,with R2 of 0.985 and 0.997,respectively.The prediction accuracy of the RBF model was better than that of the ANN:the R2 of RBF for predicted cooling and heating loads with dataset results were 0.989 and 0.992,respectively,while the R2 of ANN were 0.972 and 0.967.Using sensitivity analysis,it was found that among the 8 parameters that affecting building cool-ing and heating loads,surface area had the most significant impact,followed by wall area,roof area,and glazing area,while glazing area distribution and relative compactness had the least significant impact.
radial basic networkartificial neural networkbuildingheating and cooling loadsprediction
万方数据
维普
