A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting

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dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
dc.contributor	Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.contributor.author	Kampouropoulos, Konstantinos
dc.contributor.author	Andrade Rengifo, Fabio
dc.contributor.author	García Espinosa, Antonio
dc.contributor.author	Romeral Martínez, José Luis
dc.date	2014-02
dc.identifier.citation	Kampouropoulos, K. [et al.]. A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting. "Advances in Electrical and Computer Engineering", Febrer 2014, vol. 14, núm. 1, p. 9-14.
dc.identifier.citation	1582-7445
dc.identifier.citation	10.4316/AECE.2014.01002
dc.identifier.uri	http://hdl.handle.net/2117/22425
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria electrònica
dc.subject	Genetic algorithms
dc.subject	Adaptive neuro-fuzzy inference system
dc.subject	Energy forecast
dc.subject	Genetic algorithm
dc.subject	Intelligent energy management systems
dc.subject	Programació genètica (Informàtica)
dc.subject	Energia -- Gestió
dc.title	A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a shortterm load forecasting for the different modeled consumption processes.

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