Multi-Gene Genetic Programming for Short Term Load Forecasting

The Short Term Load Forecasting (STLF) plays a critical role in power system operation. The accuracy of the STLF is very important since it affects the generation scheduling and the electricity prices and hence an accurate STLF method should be used. This paper presents a new variant of genetic programming namely: Multi-Gene Genetic Programming (MGGP) for the problem of STLF. In order to demonstrate this technique capability, the MGGP has been compared with the RBF network and the standard single-gene Genetic Programming (GP) in terms of the forecasting accuracy. The data used in this study is a real data set of the Egyptian electrical network. The weather factors represented by the minimum and the maximum daily temperature have been included in this study. The MGGP has successfully forecasted the future load with high accuracy compared to that of the Radial Basis Function (RBF) network and that of the standard single-gene Genetic Programming (GP).

I. Introduction

The balance between the generated power and the actual demand is very important for the reliability of the power system. The generation scheduling is affected by the short term load forecasting and hence the short term load forecasting affects the reliability of the power system. The short term load forecasting also affects the economic operation of the power system. During the peak loads, the energy prices may be increased by a factor of ten or more [1]. For these reasons and since several countries have moved from the regulated energy markets to the deregulated energy markets, obtaining an accurate load forecasting became very important issue for the decision making processes of any electric utility [2]. However, having an accurate load forecasting is not an easy task due to the uncertainty imposed by the existence of exogenous variables such as the weather conditions.