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Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | Marcos Peirotén, Rodrigo Alejandro de | es-ES |
dc.contributor.author | Bello Morales, Antonio | es-ES |
dc.contributor.author | Reneses Guillén, Javier | es-ES |
dc.date.accessioned | 2017-12-21T15:52:50Z | - |
dc.date.available | 2017-12-21T15:52:50Z | - |
dc.date.issued | 2017-06-06 | es_ES |
dc.identifier.uri | http://hdl.handle.net/11531/24706 | - |
dc.description | Capítulos en libros | es_ES |
dc.description.abstract | es-ES | |
dc.description.abstract | Electricity price forecasting models are of great importance for market participants due to their considerable volatility, especially in deregulated and competitive contexts. As a result, these models are highly demanded, especially in day-to-day applications, which require not only accurate results, but also fast responsiveness. Taking these needs into account, this work proposes a novel short-term electricity forecasting approach by means of a hybrid model, combining econometric and fundamental methods. In order to validate this work’s proposed method under complex price dynamics, the model has been tested for the Iberian electricity market case, and further verified by comparing its performance with other, more traditional, forecasting models. | en-GB |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | en-GB | es_ES |
dc.publisher | Technische Universität Dresden (Dresde, Alemania) | es_ES |
dc.rights | es_ES | |
dc.rights.uri | es_ES | |
dc.source | Libro: 14th International Conference on the European Energy Market - EEM17, Página inicial: 1-6, Página final: | es_ES |
dc.subject.other | Instituto de Investigación Tecnológica (IIT) | es_ES |
dc.title | Short-term forecasting of electricity prices with a computationally efficient hybrid approach | es_ES |
dc.type | info:eu-repo/semantics/bookPart | es_ES |
dc.description.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | es_ES |
dc.keywords | es-ES | |
dc.keywords | Econometric Models, Electricity Markets, Fundamental Models, Hybrid Models, Short-Term Forecasting | en-GB |
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Fichero | Descripción | Tamaño | Formato | |
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IIT-17-064A.pdf | 818,67 kB | Adobe PDF | Visualizar/Abrir Request a copy |
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