Office building energy consumption forecast based on FA-BP combination model

Aiming at the problems of traditional BP neural network model for predicting office building energy consumption that the prediction accuracy is greatly affected by the non-linear characteristics between input variables and the generalization ability of the model is not strong, establishes an improved FA-BP neural network combination prediction model combined with the factor analysis method. Based on the actual energy consumption data, uses the factor analysis method to reduce the dimensions of the related influence factors of energy consumption, and obtains four common factors, which are used as input parameters of the neural network for empirical analysis. The results show that the improved FA-BP neural network combination model has a higher prediction accuracy than the traditional BP neural network model in the prediction of office building energy consumption, and its average relative error is 1.728%, which shows that the factor analysis method can effectively reduce the complex dimension of the input parameters of neural network.