A new methodology for nodal load forecasting in deregulated power systems

Wang, X; Hatziargyriou, N; Tsoukalas, L

dc.contributor.author	Wang, X	en
dc.contributor.author	Hatziargyriou, N	en
dc.contributor.author	Tsoukalas, L	en
dc.date.accessioned	2014-03-01T01:17:34Z
dc.date.available	2014-03-01T01:17:34Z
dc.date.issued	2002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14566
dc.subject	Load Forecasting	en
dc.subject	Power System	en
dc.title	A new methodology for nodal load forecasting in deregulated power systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/39.999661	en
heal.identifier.secondary	http://dx.doi.org/10.1109/39.999661	en
heal.publicationDate	2002	en
heal.abstract	A neurofuzzy methodology for online nodal load prediction is introduced that exploits the power of artificial neural networks (ANN) and fuzzy logic. ANNs are used to capture the power consumption patterns specific to a customer, while a fuzzy logic module detects departures from equilibrium (that is, previously established consumption patterns). The fuzzy-logic-based (FL) module (called PROTREN) performs signal trend identification.	en
heal.journalName	IEEE Power & Energy Magazine	en
dc.identifier.doi	10.1109/39.999661	en