A Comparison of Two Techniques for Next- Day Electricity Price Forecasting

Troncoso Lora, Alicia; Riquelme Santos, Jesús Manuel; Riquelme Santos, José Cristóbal; Gómez Expósito, Antonio; Martínez Ramos, José Luis

doi:http://dx.doi.org/10.1007/3-540-45675-9_57

Capítulo de Libro

dc.creator	Troncoso Lora, Alicia	es
dc.creator	Riquelme Santos, Jesús Manuel	es
dc.creator	Riquelme Santos, José Cristóbal	es
dc.creator	Gómez Expósito, Antonio	es
dc.creator	Martínez Ramos, José Luis	es
dc.date.accessioned	2016-03-30T10:41:53Z
dc.date.available	2016-03-30T10:41:53Z
dc.date.issued	2002
dc.identifier.citation	Troncoso Lora, A., Riquelme Santos, J.M.,...,Martínez Ramos, J.L. (2002). A Comparison of Two Techniques for Next- Day Electricity Price Forecasting. En Intelligent Data Engineering and Automated Learning — IDEAL 2002, Lecture Notes in Computer Science, Volume 2412, pp 384-390 (2002) .
dc.identifier.uri	http://hdl.handle.net/11441/39155
dc.description.abstract	In the framework of competitive markets, the market’s participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported.	es
dc.format	application/pdf	es
dc.language.iso	eng	es
dc.relation.ispartof	Intelligent Data Engineering and Automated Learning — IDEAL 2002, Lecture Notes in Computer Science, Volume 2412, pp 384-390 (2002)	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Artificial Intelligence (incl. Robotics)	es
dc.subject	Data Structures	es
dc.subject	Cryptology and Information Theory	es
dc.subject	Information Storage and Retrieval	es
dc.subject	Information Systems Applications (incl. Internet)	es
dc.subject	Pattern Recognition	es
dc.subject	Database Management	es
dc.title	A Comparison of Two Techniques for Next- Day Electricity Price Forecasting	es
dc.type	info:eu-repo/semantics/bookPart	es
dcterms.identifier	https://ror.org/03yxnpp24
dc.type.version	info:eu-repo/semantics/publishedVersion	es
dc.rights.accessRights	info:eu-repo/semantics/openAccess	es
dc.contributor.affiliation	Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos	es
dc.contributor.affiliation	Universidad de Sevilla. Departamento de Ingeniería Eléctrica	es
dc.identifier.doi	http://dx.doi.org/10.1007/3-540-45675-9_57	es
dc.identifier.idus	https://idus.us.es/xmlui/handle/11441/39155

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