Abstract: Dependence of medium and long-term load variation on socio-economic indicators is difficult to express by an accurate mathematical model． This paper applied data mining techniques to the association analysis of the total electricity consumption growth.Multiple indicators were selected from the socio-economic indicators since 2000to compose relevant factors database,and the missing data were completed.Several indicators closely related to total electricity consumption were mined using cluster analysis,and the distortion data were corrected,thus a more scientific load forecasting model was constructed. Through time series of dynamic neural network,the load forecasting model was tested and validated．The results show that the prediction model has good convergence and satisfactory effect.

Key words: Annual load forecast,Data mining,Data completion,Data correction,NARX neural network