01382nam 2200433I 450 991069348710332120121001182500.0(CKB)4940000000464978(OCoLC)812451264ocm45954733(OCoLC)45954733(OCoLC)622392341(OCoLC)964879282(OCoLC)971073661(OCoLC)980995804(OCoLC)988824822(OCoLC)993514611(OCoLC)61324147(VaAlASP)PL038467(EXLCZ)99494000000046497820121002d2012 uy 0engur|n||||||||atxtrdacontentcrdamediacrrdacarrierThe News[First electronic edition].Washington, D.C. U.S. Dept. of Labor, Bureau of Labor StatisticsAlexandria, VA :Alexander Street Press,2012.1 online resourceTitle from HTML title page (viewed October 1, 2012).Alexander Street Press first edition.American dramaUnited StatesfastDrama.lcgftAmerican drama.Aronson Billy1352928United States.Bureau of Labor Statistics.VaAlASPVaAlASPBOOK9910693487103321The News3209779UNINA02975nam 2200769z- 450 991057688340332120220621(CKB)5720000000008340(oapen)https://directory.doabooks.org/handle/20.500.12854/84505(oapen)doab84505(EXLCZ)99572000000000834020202206d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Methods of Power Load ForecastingBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (128 p.)3-0365-4218-3 3-0365-4217-5 This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.PhysicsbicsscResearch and information: generalbicsscArtificial Neural Networkattentionbidirectional long short-term memoryCNNdeep learningdeep neural networkdemandDIMSencoder decoderforecastgalvanizingHolt-Winters modelirregularloadlong-term forecastingLSTMmachine learningmulti-layer stackedmultiple seasonalityneural networkonline trainingparameters tuningpeak loadpower systemprophet modelProphet modelrecurrent neural networkshort-term electrical load forecastingshort-term load forecastshort-term load forecastingstatistical analysistime seriesPhysicsResearch and information: generalGarcía-Díaz J. Carlosedt1323492Trull ÓscaredtGarcía-Díaz J. CarlosothTrull ÓscarothBOOK9910576883403321Advanced Methods of Power Load Forecasting3035623UNINA