Short Term Load Forecasting using a Neural Network trained by A Hybrid Artificial Immune System

Abstract

Short term load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of power system. Artificial neural networks are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. These are generally trained through back-propagation, genetic algorithm (GA), particle swarm optimization (PSO) and artificial immune system (AIS). All these algorithms have specific benefits in terms of accuracy, speed of convergence and historical data requirement for training. In this paper a hybrid AIS is proposed, which is a combination of back-propagation with AIS to get faster convergence, lesser historical data requirement for training with a little compromise in accuracy.