000060663 001__ 60663
000060663 005__ 20200221144337.0
000060663 0247_ $$2doi$$a10.3390/en9090721
000060663 0248_ $$2sideral$$a98055
000060663 037__ $$aART-2016-98055
000060663 041__ $$aeng
000060663 100__ $$aMonteiro, Claudio
000060663 245__ $$aShort-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market
000060663 260__ $$c2016
000060663 5060_ $$aAccess copy available to the general public$$fUnrestricted
000060663 5203_ $$aThis paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.
000060663 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/ENE2013-48517-C2-1-R$$9info:eu-repo/grantAgreement/ES/MINECO/ENE2013-48517-C2-2-R
000060663 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000060663 590__ $$a2.262$$b2016
000060663 591__ $$aENERGY & FUELS$$b45 / 92 = 0.489$$c2016$$dQ2$$eT2
000060663 592__ $$a0.662$$b2016
000060663 593__ $$aElectrical and Electronic Engineering$$c2016$$dQ1
000060663 593__ $$aRenewable Energy, Sustainability and the Environment$$c2016$$dQ2
000060663 593__ $$aEnergy Engineering and Power Technology$$c2016$$dQ2
000060663 593__ $$aControl and Optimization$$c2016$$dQ2
000060663 593__ $$aEnergy (miscellaneous)$$c2016$$dQ2
000060663 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000060663 700__ $$0(orcid)0000-0002-5502-4232$$aRamirez-Rosado, Ignacio J.$$uUniversidad de Zaragoza
000060663 700__ $$aFernandez-Jimenez, A.
000060663 700__ $$aConde, Pedro
000060663 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000060663 773__ $$g9, 9 (2016), [24 pp.]$$pENERGIES$$tEnergies$$x1996-1073
000060663 8564_ $$s3090092$$uhttps://zaguan.unizar.es/record/60663/files/texto_completo.pdf$$yVersión publicada
000060663 8564_ $$s107730$$uhttps://zaguan.unizar.es/record/60663/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000060663 909CO $$ooai:zaguan.unizar.es:60663$$particulos$$pdriver
000060663 951__ $$a2020-02-21-13:48:12
000060663 980__ $$aARTICLE