Neural-Fuzzy Approach for Power Load Forecasting Analysis

This paper presents Neuro-Fuzzy approach for forecasting analysis in power load. Forecasting the power load is a difficult task for a country and both positive and negative load forecasting makes a big problem for the country. An approach that Neuro-Fuzzy model is proposed for forecast power load in this paper. The proposed model a fuzzy back propagation network is constructed and then a fuzzy intersection is applied and after that de-fuzzify the result to generate a crisp value by using Radial Basis Function network (RBF). The proposed model improves the accuracy of power load forecasting. The forecasted results obtained by neuro-fuzzy method were compared with the Artificial Neural Network by using Mean Absolute Percentage Error (MAPE) to measure accuracy of the result. The experimental result shows that the neuro-fuzzy implementations have more accuracy.