Parameter Estimation Based On Particle Swarm Optimization for Short Term Load Forecasting

Abstract

Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. The algorithms and networks were having been demonstrated using simulation studies. The techniques proposed in this thesis have been simulated using data obtained from State Load Dispatch Centre, Ablowal, Punjab and Rajasthan for the duration of one week and technique is used to estimate the parameters of linear and quadratic model and the results obtain for peak load forecasting are compared with the least error square method.