Abstract
Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.
Highlights
Short Term Load Forecast (STLF) has gone under constant improvements for the last few decades because of its great importance in economic growth of a country
In order to overcome the deficiencies found in the existing STLF models, a new approach is proposed which integrates the genetic algorithms and artificial neural networks for the development of STLF model
The results obtained from training and testing the neural network on historical load data of one month period sampled at half hour frequency are presented below in graphical form (Fig. 6)
Summary
STLF has gone under constant improvements for the last few decades because of its great importance in economic growth of a country. Artificial Neural Network (ANN) based models are Support vector machines: Support Vector Machine most frequently deployed and has shown very (SVM) is a powerful and recent technique for the promising results in STLF. This is because of their solution of classification and regression problems. Srinivasan (1998), employed back propagation ANN-based model and genetic algorithm to evolve the optimal neural network structure. This approach is powerful despites the fact that the model is unable to detect sudden load changes.
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