Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach

This research develops a weighted evolving fuzzy neural network for monthly electricity demand forecasting in Taiwan. This study modifies the evolving fuzzy neural network framework (EFuNN framework) by adopting a weighted factor to calculate the importance of each factor among the different rules. In addition, an exponential transfer function (exp(−D)) is employed to transfer the distance of any two factors to the value of similarity among different rules, thus a different rule clustering method is developed accordingly. Seven factors identified by the Taiwan Power Company will affect the power consumption in Taiwan. These seven factors will be inputted into the WEFuNN to forecast the electricity demand of the future. The historical data will be used to train the WEFuNN. After training, the trained model will forecast the future electricity demands. Finally, the WEFuNN model is compared with other approaches, which are proposed in the literature. The experimental results reveal that the MAPE for WEFuNN model is 6.43% which is better than the MAPE value for other approaches. Thus, the WEFuNN model is more accurate in forecasting the monthly electricity demand than the other approaches. In summary, the WEFuNN model can be practically applied as an electricity demand forecasting tool in Taiwan.