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dc.contributor.authorMishra, S-
dc.contributor.authorPatra, S K-
dc.identifier.citationIEEE Region 10 and the Third international Conference on Industrial and Information Systems, Kharagpur, India December 8-10, 2008en
dc.description.abstractShort 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 non-linear 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.en
dc.format.extent205100 bytes-
dc.subjectGenetic algorithmen
dc.subjectParticle swarm optimizationen
dc.subjectArtificial immune systemen
dc.subjectShort term load forecastingen
dc.titleShort Term Load Forecasting using a Neural Network Trained by a Hybrid Artifical Immune Systemen
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