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An Emotional Neural Network for Electrical Load Demand Forecast

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dc.contributor.author ul Islam, Badar
dc.contributor.author Arain, Salman
dc.contributor.author Quudus, Asim
dc.date.accessioned 2022-10-19T07:04:19Z
dc.date.available 2022-10-19T07:04:19Z
dc.date.issued 2018-12-17
dc.identifier.citation ul Islam, B., Arain, S., & Quudus, A. (2018). An Emotional Neural Network for Electrical Load Demand Forecast. NFC IEFR Journal of Engineering and Scientific Research, 6, 155-159. en_US
dc.identifier.uri http://142.54.178.187:9060/xmlui/handle/123456789/13325
dc.description.abstract Emotional neural network (EmNN) is a new approach that implements the virtual emotions to support the learning process of neural networks. The inspiration of EmNN is adopted from neurophysiological studies of the human brain behaviors under emotional circumstances. In this research, EmNN based models are designed and experimented for electrical load forecasting application. The numerical parameters are fine-tuned by applying genetic algorithm as an optimization tool. Two case studies are developed with different data sets for the training and testing of the proposed model.A hybrid input variable selection method is proposed for identifying and implementing the most appropriate input variables in the learning process. A couple of conventional training algorithms of ANN are employed for the same datasets and the outcomesare compared with EmNN model. The results of the proposed model show that the suggested techniqueperformed better as compared to conventional ANN with respect to prediction accuracy and generalization en_US
dc.language.iso en en_US
dc.publisher Faisalabad:NFC Institute of Engineering and Fertilizer Research Jaranwala Road, Faisalabad en_US
dc.subject Artificial neural network en_US
dc.subject Emotional neural network en_US
dc.subject Short term load forecasting en_US
dc.subject Correlation analysis en_US
dc.subject Genetic Algorithm en_US
dc.title An Emotional Neural Network for Electrical Load Demand Forecast en_US
dc.type Article en_US


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