Short Term Load Forecasting using Computational Intelligence Methods

Abstract

Electrical load forecasting provides very important informations to take useful decisions for newlinegeneration, control, power dispatch, maintenance and expansion of power facility. Accurate short newlineterm load forecasting (STLF) results in economic and trouble free operations. STLF improves newlineefficiency of power system with accurate load scheduling and reduction in power system reserves. newlineSTLF enhances reliability of power grid by reducing possibility of overloading and blackouts. newlineElectrical load forecasting is a challenging task due to different unstable factors, like weather newlinevariables, social activities, dynamic electricity prices and nonlinear behaviour of consumer demand. newlineRegional weather variables have significant effect on electrical load demand. Presented research newlinework is an effort to develop a short term load forecasting approach for Rajasthan state region using newlinecomputational intelligence methodologies. Rajasthan state has been selected for research purpose newlinebecause, no literature is available related to development of STLF models for this region till date. newlineRajasthan region is the biggest in land area in India, having area of 342,239 km² with population of newlineapproximate 85 million. Rajasthan state region has extreme climatic conditions with geological newlinediversities, less industrialization and rich cultural heritage. Approximate half of the region suffers newlinelack of rain and face a temperature variation from -2and#8451; and#119905;and#119900; 48and#8451;. In Rajasthan, there is a big gap newlinebetween electrical load demand and supply and this gap is increasing continuously. Electrical load newlinedemand of Rajasthan, mainly depends upon weather parameters, rain, types of crop, cultivated area, newlinedomestic load, commercial demand and load of small scale industries. newline