Title :
Multi-Gene Genetic Programming for Short Term Load Forecasting
Author :
Ghareeb, W.T. ; El Saadany, E.F.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada
Abstract :
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).
Keywords :
genetic algorithms; load forecasting; power engineering computing; radial basis function networks; Egyptian electrical network; RBF network; STLF method; electricity prices; forecasting accuracy; generation scheduling; multigene genetic programming; power system operation; radial basis function; short term load forecasting; standard single-gene genetic programming; technique capability; weather factors; Computational modeling; Equations; Genetics; Load modeling; Mathematical model; Programming; Radial basis function networks; Short-term load forecasting; genetic programming; multi-gene genetic programming; radial basis function;
Conference_Titel :
Electric Power and Energy Conversion Systems (EPECS), 2013 3rd International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4799-0687-1
DOI :
10.1109/EPECS.2013.6713061
