Research and application of data mining and NARX neural networks in load forecasting

The relationship between med-long term load forecasting and socio-economic indicators is very difficult to describe with an accurate mathematical model. The paper introduce data mining technology into the association analysis of China's electricity consumption growth, select many socio-economic indicators since 2000, constitute the relevant factors database, complement of a few missing data, and dig out a number of indicators closely related to the electricity consumption with cluster analysis, and the data of distortion indicators is corrected, thus, build a more scientific load forecasting model. Validate and test the correlation of electricity consumption and selected indicators by dynamic neural network time sequence tool. The results show that the prediction model has good convergence, and the effect is satisfactory. © 2014 SERSC.