Energy and Power Engineering, 2011, 3, 190-193 doi:10.4236/epe.2011.32024 Published Online May 2011 (http://www.SciRP.org/journal/epe) Copyright © 2011 SciRes. EPE A Hybrid Short Term Load Forecasting Model of an Indian Grid Rabindra Behera 1 , Bibhu Prasad Panigrahi 1 , Bibhuti Bhusan Pati 2 1 Department of Electrical Engineering I. G. I. T. Sarang, Orissa, India 2 Department of Electrical Engineering VSSUT Burla, Orissa, India E-mail: b_rabindra@yahoo.co.in, bibhu89@yahoo.com, pati_bibhuti@rediffmail.com Received March 21, 2011; revised March 28, 2011; accepted April 8, 2011 Abstract This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec- trical load forecasting considering the factors, past data of the load, respective weather condition and finan- cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel- oping country should be ignored as the development of a country is highly unpredictable. However, the im- portance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors. Keywords: Short Term Load Forecasting, Parameter Estimation, Trending Technique, Co-Relation 1. Introduction Electrical energy is a superior form of energy for all types of consumer needs. The close tracking of system generation at all time is the basic requirement in the op- eration of power system. There is a 3% - 7% of increase of electrical load per year for many years. Short-term load forecasting (STLF) is essential for an effective en- ergy management in a deregulated power open market. However, the electric power load forecasting problem is not easy to handle due to nonlinear and random-like be- haviors of system loads, weather conditions, and varia- tions of social and economic environments. A wide variety of models have been proposed in the last two decades for STLF due to its importance etc. A wide variety of models have been proposed in the last two decades for STLF due to its importance, such as Functional clustering and linear regression for peak load forecasting [1], Mixed price and load forecasting of elec- tricity markets by a new iterative prediction method [2] and univariate modeling and forecasting of monthly en- ergy demand time series using abductive and neural networks [3] etc. Moreover, the electrical load forecast- ing depends on many known and unknown variables. These variables can be considered to have steady values within a specified region under one electricity regulatory authority. This type of load forecasting is being termed as spatial load forecasting. Aldo Goia, Caterina May and Gianluca Fusai in their paper “Functional clustering and linear regression for peak load forecasting” suggested a new approach using past heating demand data in a district-heating system. Nima Amjadya, Ali Daraeepour in their paper “Mixed price and load forecasting of electricity markets by a new iterative prediction method” suggested real conditions of an electricity market and short-term load forecasting. R. E. Abdel-Aalin’s paper “Univariate modeling and Fore- casting of Monthly Energy Demand Time Series Using Abductive and Neural Networks” suggested univariate modeling of the monthly demand time series based only on data for 6 years to forecast the demand for the seventh year contrary to multivariate models. Here the load and weather data of Bhubaneswar (India) power grid has been collected for six consecutive years. Also the economic growth of the people is studied. It has been observed that economic growth in short term can be considered as negligible. A Case study has been conducted on an Indian grid located at Bhubaneswar, Orissa based on previous load and weather data.