Probabilistic electricity price forecasting models by aggregation of competitive predictors

This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price fore...

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Autores: Monteiro, C. [0000-0003-4858-647X], Ramirez-Rosado, I.J. [0000-0002-5502-4232], Fernandez-Jimenez, L.A. [0000-0002-5633-4849]
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Universidad de La Rioja (UR)
Repositorio:RIUR. Repositorio Institucional de la Universidad de La Rioja
OAI Identifier:oai:portal.dialnet.es:doc/5bbc690db750603269e81432
Acceso en línea:https://investigacion.unirioja.es/documentos/5bbc690db750603269e81432
Access Level:acceso abierto
Palabra clave:Daily session prices
Electricity market prices
Electricity price forecasting
Iberian Electricity Market (MIBEL)
Probabilistic forecast
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spelling Probabilistic electricity price forecasting models by aggregation of competitive predictorsMonteiro, C. [0000-0003-4858-647X]Ramirez-Rosado, I.J. [0000-0002-5502-4232]Fernandez-Jimenez, L.A. [0000-0002-5633-4849]Daily session pricesElectricity market pricesElectricity price forecastingIberian Electricity Market (MIBEL)Probabilistic forecastThis article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.2018info:eu-repo/semantics/articleSubtype: Articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://investigacion.unirioja.es/documentos/5bbc690db750603269e81432reponame:RIUR. Repositorio Institucional de la Universidad de La Riojainstname:Universidad de La Rioja (UR)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.3390/EN11051074info:eu-repo/semantics/altIdentifier/pissn/1996-1073Probabilistic electricity price forecasting models by aggregation of competitive predictors, 2018, vol. 11, núm. 5info:eu-repo/semantics/openAccessoai:portal.dialnet.es:doc/5bbc690db750603269e814322026-06-14T12:47:17Z
dc.title.none.fl_str_mv Probabilistic electricity price forecasting models by aggregation of competitive predictors
title Probabilistic electricity price forecasting models by aggregation of competitive predictors
spellingShingle Probabilistic electricity price forecasting models by aggregation of competitive predictors
Monteiro, C. [0000-0003-4858-647X]
Daily session prices
Electricity market prices
Electricity price forecasting
Iberian Electricity Market (MIBEL)
Probabilistic forecast
title_short Probabilistic electricity price forecasting models by aggregation of competitive predictors
title_full Probabilistic electricity price forecasting models by aggregation of competitive predictors
title_fullStr Probabilistic electricity price forecasting models by aggregation of competitive predictors
title_full_unstemmed Probabilistic electricity price forecasting models by aggregation of competitive predictors
title_sort Probabilistic electricity price forecasting models by aggregation of competitive predictors
dc.creator.none.fl_str_mv Monteiro, C. [0000-0003-4858-647X]
Ramirez-Rosado, I.J. [0000-0002-5502-4232]
Fernandez-Jimenez, L.A. [0000-0002-5633-4849]
author Monteiro, C. [0000-0003-4858-647X]
author_facet Monteiro, C. [0000-0003-4858-647X]
Ramirez-Rosado, I.J. [0000-0002-5502-4232]
Fernandez-Jimenez, L.A. [0000-0002-5633-4849]
author_role author
author2 Ramirez-Rosado, I.J. [0000-0002-5502-4232]
Fernandez-Jimenez, L.A. [0000-0002-5633-4849]
author2_role author
author
dc.subject.none.fl_str_mv Daily session prices
Electricity market prices
Electricity price forecasting
Iberian Electricity Market (MIBEL)
Probabilistic forecast
topic Daily session prices
Electricity market prices
Electricity price forecasting
Iberian Electricity Market (MIBEL)
Probabilistic forecast
description This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
Subtype: Article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://investigacion.unirioja.es/documentos/5bbc690db750603269e81432
url https://investigacion.unirioja.es/documentos/5bbc690db750603269e81432
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/EN11051074
info:eu-repo/semantics/altIdentifier/pissn/1996-1073
Probabilistic electricity price forecasting models by aggregation of competitive predictors, 2018, vol. 11, núm. 5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:RIUR. Repositorio Institucional de la Universidad de La Rioja
instname:Universidad de La Rioja (UR)
instname_str Universidad de La Rioja (UR)
reponame_str RIUR. Repositorio Institucional de la Universidad de La Rioja
collection RIUR. Repositorio Institucional de la Universidad de La Rioja
repository.name.fl_str_mv
repository.mail.fl_str_mv
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