A New Approach for Next Day Load Forecasting Integrating Artificial Neural Network Model with Weighted Frequency Bin Blocks

Abstract

In this study, a new method is developed for the next day load forecasting integrating Artificial Neural Network(ANN) model with Weighted Frequency Bin Blocks (WFBB). After the WFBB is applied to all data, the results obtained from this analysis are used as the inputs in the ANN structure. However, the conventional ANN structure is also used for the next day load forecasting. The forecasting results obtained from ANN structure and the hybrid model are compared in the sense of root mean square error (RMSE). It is observed that the performance and the RMSE values for the hybrid model,the ANN model with WFBB, are smaller than the values for the conventional ANN structure. Furthermore, the new hybrid model forecasts better than the conventional ANN structure. The suitability of the proposed approach is illustrated through an application to actual load data taken from the Turkish Electric Power Company in 2002.KeywordsRoot Mean Square ErrorDirect Memory AccessLoad ForecastFast Fourier Transform AlgorithmInverse Fast Fourier TransformThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.