000070970 001__ 70970
000070970 005__ 20190709135635.0
000070970 0247_ $$2doi$$a10.3390/en11051074
000070970 0248_ $$2sideral$$a106621
000070970 037__ $$aART-2018-106621
000070970 041__ $$aeng
000070970 100__ $$aMonteiro, C.
000070970 245__ $$aProbabilistic electricity price forecasting models by aggregation of competitive predictors
000070970 260__ $$c2018
000070970 5060_ $$aAccess copy available to the general public$$fUnrestricted
000070970 5203_ $$aThis 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.
000070970 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/ENE2015-70032-REDT$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/ENE2016-78509-C3-3-P
000070970 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000070970 590__ $$a2.707$$b2018
000070970 591__ $$aENERGY & FUELS$$b56 / 103 = 0.544$$c2018$$dQ3$$eT2
000070970 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000070970 700__ $$0(orcid)0000-0002-5502-4232$$aRamirez-Rosado, I.J.$$uUniversidad de Zaragoza
000070970 700__ $$aFernandez-Jimenez, L.A.
000070970 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000070970 773__ $$g11, 5 (2018), 1074 [25 pp]$$pENERGIES$$tEnergies$$x1996-1073
000070970 8564_ $$s1129330$$uhttp://zaguan.unizar.es/record/70970/files/texto_completo.pdf$$yVersión publicada
000070970 8564_ $$s110507$$uhttp://zaguan.unizar.es/record/70970/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000070970 909CO $$ooai:zaguan.unizar.es:70970$$particulos$$pdriver
000070970 951__ $$a2019-07-09-12:35:30
000070970 980__ $$aARTICLE