Computational intelligence techniques for short-term electric load forecasting

Tzafestas, S; Tzafestas, E

dc.contributor.author	Tzafestas, S	en
dc.contributor.author	Tzafestas, E	en
dc.date.accessioned	2014-03-01T01:16:14Z
dc.date.available	2014-03-01T01:16:14Z
dc.date.issued	2001	en
dc.identifier.issn	0921-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13995
dc.subject	Chaos	en
dc.subject	Computational intelligence	en
dc.subject	Fuzzy logic	en
dc.subject	Genetic algorithms	en
dc.subject	Neural networks	en
dc.subject	Short-term load forecasting	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Robotics	en
dc.subject.other	Chaos theory	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Electric power plants	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Neural networks	en
dc.subject.other	Knowledge based forecasting	en
dc.subject.other	Model based forecasting	en
dc.subject.other	Short term electric load forecasting	en
dc.subject.other	Artificial intelligence	en
dc.title	Computational intelligence techniques for short-term electric load forecasting	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/A:1012402930055	en
heal.identifier.secondary	http://dx.doi.org/10.1023/A:1012402930055	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	Electric load forecasting has received an increasing attention over the years by academic and industrial researchers and practitioners due to its major role for the effective and economic operation of power utilities. The aim of this paper is to provide a collective unified survey study on the application of computational intelligence (CI) model-free techniques to the short-term load forecasting of electric power plants. All four classes of CI methodologies, namely neural networks (NNs), fuzzy logic (FL), genetic algorithms (GAs) and chaos are addressed. The paper starts with some background material on model-based and knowledge-based forecasting methodologies revealing a number of key issues. Then, the pure NN-based and FL-based forecasting methodologies are presented in some detail. Next, the hybrid neurofuzzy forecasting methodology (ANFIS, GARIC and Fuzzy ART variations), and three other hybrid CI methodologies (KB-NN, Chaos-FL, Neurofuzzy-GA) are reviewed. The paper ends with eight representative case studies, which show the relative merits and performance that can be achieved by the various forecasting methodologies under a large repertory of geographic, weather and other peculiar conditions. An overall evaluation of the state-of-art of the field is provided in the conclusions.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	Journal of Intelligent and Robotic Systems: Theory and Applications	en
dc.identifier.doi	10.1023/A:1012402930055	en
dc.identifier.isi	ISI:000171806500002	en
dc.identifier.volume	31	en
dc.identifier.issue	1-3	en
dc.identifier.spage	7	en
dc.identifier.epage	68	en