A New Approach for Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms

This paper presents a new approach for short-term load forecasting (STLF). Curve fitting prediction and time series models are used for hourly loads forecasting of the week days. The curve fitting prediction (CFP) technique combined with genetic algorithms (GAs) is used for obtaining the optimum parameters of Gaussian model to obtain a minimum error between actual and forecasted load. A new technique for selecting the training vectors is introduced. The proposed model is simple, fast, and accurate. It is shown that the proposed approach provide very accurate hourly load forecast. Also it is shown that the proposed method can provide more accurate results. The mean percent relative error of the model is less than 1 %. Index Term: Load Forecasting, Curve Fitting Prediction, Genetic Algorithms, Short-Term I. INTRODUCTION In recent years, with the opening of electricity market, electrical power systems load forecasting play an important role for electrical power operation. Accurate load forecast will lead to appropriate operation and planning for the power system, thus achieving a lower operating cost and higher reliability of electricity supply. Short-term load forecasting (STLF) of electric power not only plays a very important role in operation scheduling, like economic emission dispatch, unit commitment, energy transactions, and fuel purchasing, but also has a significant impact on the secure operation of power system (1). Short-term load forecasting aims to predict electric loads for a period of minutes, hours, days or weeks. The quality of the short-term load forecasts with lead time ranging from one hour to several days ahead has a significant impact on the efficiency of operation of any power utility (2- 4). The objectives of the STLF are (5): 1. To derive the scheduling function that determines the most economic load dispatch with operational constraints and policies, environmental and equipment limitations. 2. To insure the security of the power system at any time point.