A hybrid genetic radial basis function network with fuzzy corrector for short term load forecasting

The short term load forecasting plays a critical role in power system operation and economics. The accuracy of short term load forecasting is very important since it affects generation scheduling and electricity prices, and hence an accurate short term load forecasting method should be used. This paper proposes a Genetic Algorithm optimized Radial Basis Function network (GA-RBF) with a fuzzy corrector for the problem of short term load forecasting. In order to demonstrate this system capability, the system has been compared with four well known techniques in the area of load forecasting. These techniques are the multi-layer feed forward neural network, the RBF network, the adaptive neuro-fuzzy inference System and the genetic programming. The data used in this study is a real data of the Egyptian electrical network. The weather factors represented in the minimum and the maximum daily temperature have been included in this study. The GA-RBF with the fuzzy corrector has successfully forecasted the future load with high accuracy compared to that of the other load forecasting techniques included in this study.