Enabling external factors for consumption electricity forecasting using hybrid genetic algorithm and fuzzy neural system

Forecasting of the future load is important because of dramatic changes occurring in the electricity consumption lifestyle. Several algorithms have been suggested for solving this problem. This paper introduces a new modified fuzzy neural system approach for short term load forecasting. By using two phase on Fuzzy Inference system and Genetic algorithm for optimization, weight can improve the accuracy of electricity load forecasting. The relationship external factors like temperature, humidity, price load, Gross Domestic Product and load is identified with a case study for a particular region. Data for a monthly load of five years has been used. The accuracy algorithm has been validated using Root Mean Square Error (RMSE). The result RMSE is 0.78 it is shown that our proposed method is feasible.