Artificial intelligence in short term electric load forecasting: A state-of-the-art survey for the researcher

Metaxiotis, K; Kagiannas, A; Askounis, D; Psarras, J

dc.contributor.author	Metaxiotis, K	en
dc.contributor.author	Kagiannas, A	en
dc.contributor.author	Askounis, D	en
dc.contributor.author	Psarras, J	en
dc.date.accessioned	2014-03-01T01:18:41Z
dc.date.available	2014-03-01T01:18:41Z
dc.date.issued	2003	en
dc.identifier.issn	0196-8904	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15155
dc.subject	Artificial intelligence	en
dc.subject	Electric load forecasting	en
dc.subject	Energy	en
dc.subject.classification	Thermodynamics	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Mechanics	en
dc.subject.classification	Physics, Nuclear	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Energy conservation	en
dc.subject.other	Problem solving	en
dc.subject.other	Research	en
dc.subject.other	Short term electric load forecasting (STELF)	en
dc.subject.other	Electric load forecasting	en
dc.title	Artificial intelligence in short term electric load forecasting: A state-of-the-art survey for the researcher	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0196-8904(02)00148-6	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0196-8904(02)00148-6	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. AI-based systems are being developed and deployed worldwide in myriad applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. This paper provides an overview for the researcher of AI technologies, as well as their current use in the field of short term electric load forecasting (STELF). The history of AI in STELF is outlined, leading to a discussion of the various approaches as well as the current research directions. The paper concludes by sharing thoughts and estimations on AI future prospects in this area. This review reveals that although still regarded as a novel methodology, AI technologies are shown to have matured to the point of offering real practical benefits in many of their applications. (C) 2002 Elsevier Science Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Energy Conversion and Management	en
dc.identifier.doi	10.1016/S0196-8904(02)00148-6	en
dc.identifier.isi	ISI:000180612100012	en
dc.identifier.volume	44	en
dc.identifier.issue	9	en
dc.identifier.spage	1525	en
dc.identifier.epage	1534	en