Heating load forecast based on influencing factors analysis and wavelet neural network

Analyses the correlation between influencing factors and heating load from the measured values of time series by statistical methods. Nonlinear dynamic characteristics hidden in the central heating system can be analysed through using the wavelet analysis that can effectively extract local information of time series, combined with neural network. Establishes the wavelet neural network forecast model. Divides the influencing factors of heating load into two groups, i.e. the higher correlation group (including circulation flow rate, supply water temperature and return water pressure) and the lower correlation group (including supply and return water pressure and return water temperature).The forecast results show that the fitting degree of the higher correlation group is higher than that of the lower correlation group. Compares the accuracy of the forecasted value with that by BP neural network. The results show that the wavelet neural network heating load forecast method based on influencing factor analysis and gradient revision has better dynamic characteristics, higher generalization ability and accuracy, and it is suitable for short-term forecast of heating load.