Comparative Study of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning and Radial Basis Functional Neural Network

Abstract

The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling and for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning and operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling and unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flow and to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability and to the reduced occurrences of equipment failures and blackouts.
The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.