Arificial Neural Network

In: Computers and Technology

Submitted By safwan
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A Review of ANN-based Short-Term Load Forecasting Models
Y. Rui A.A. El-Keib

Department of Electrical Engineering University of Alabama, Tuscaloosa, AL 35487

Abstract - Artificial Neural Networks (AAN) have recently been receiving considerable attention and a large number of publications concerning ANN-based short-term load forecasting (STLF) have appreared in the literature. An extensive survey of ANN-based load forecasting models is given in this paper. The six most important factors which affect the accuracy and efficiency of the load forecasters are presented and discussed. The paper also includes conclusions reached by the authors as a result of their research in this area. Keywords: artificial neural networks, short-term load forecasting models

Introduction
Accurate and robust load forecasting is of great importance for power system operation. It is the basis of economic dispatch, hydro-thermal coordination, unit commitment, transaction evaluation, and system security analysis among other functions. Because of its importance, load forecasting has been extensively researched and a large number of models were proposed during the past several decades, such as Box-Jenkins models, ARIMA models, Kalman filtering models, and the spectral expansion techniques-based models. Generally, the models are based on statistcal methods and work well under normal conditions, however, they show some deficiency in the presence of an abrupt change in environmental or sociological variables which are believed to affect load patterns. Also, the employed techniques for those models use a large number of complex relationships, require a long computational time, and may result in numerical instabilities. Therefore, some new forecasting models were introduced recently. As a result of the development of Artificial Intelligence (AI), Expert System (ES) and Artificial Neural…...

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