A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting

This paper proposes an effective fusion of neural networks and grey modeling for adaptive electricity load forecasting. The fusion employs the complementary strength of these two appealing techniques. In terms of forecasting accuracy, the proposed fusion scheme outperforms the individual ones and the statistical autoregressive methods according to the results of a substantial number of experiments. In addition to the fusion scheme, this paper also proposes a grey relational analysis to automatically assess the importance of each input variable for the forecasting task. This analysis helps the forecaster choose dominant ones among the many input variables, thus removing much burden of acquiring professional domain knowledge for problems and reducing the interference of irrelevant inputs on the forecasting. Experimental results are shown in this paper to verify the effectiveness of the grey relational analysis.