Load Forecasting in Power Systems Using Emotional Learning

Load forecasting is an important problem in the operation and planning of electrical power generation. To minimize the operating cost, electric supplier will use forecasted load to control the number of running generator unit. Short-term load forecasting (STLF) is for hour to hour forecasting and important to daily maintaining of power plant. Most important factors in load forecasting includes past load history, calendar information (weekday, weekend, holiday, season, etc.) and weather information (instant temperature, average temperature, peak temperature, wind speed, etc.). The forecaster will treat past data as a time series and many kinds of approaches have been applied on this problem. In this paper we present an application of emotional learning to short term load forecasting. Emotional learning is a family of intelligent algorithms which can be used for time series prediction, classification, control and identification. This method is applied to short term load forecasting for the Hormozgan Regional Electric Co. (HREC). The method is relatively simple, and effectively uses historical data to provide load forecasts. Simulation results confirm good accuracy of the emotional learning approach to load forecasting. Index-TermsLoad forecasting, Emotional learning, power system, critic, neuofuzzy