01024nam0-2200361---450-99000826696040332120140521111404.00-12-065202-1000826696FED01000826696(Aleph)000826696FED0100082669620060206d2000----km-y0itay50------baengUSa-------001yyProbability and measure theoryRobert B. Ashwith contributions from Catherine Doleans-Dade2nd ed.San DiegoHarcourt Academic Press2000XII, 516ill.23 cmProbabilitàAnalisi matematica519.2Ash,Robert B.<1935- >85ITUNINARICAUNIMARCBK99000826696040332110 B II 377DIS 4937DINEL04 031-95DIC 4365DINCHDINCHDINELProbability and measure theory742614UNINA00804nam0-2200265 --450 991031345610332120190401165136.0978131650762920190401d--------kmuy0itay5050 baengGBa 001yyWriting metamorphosis in the english Renaissance1550-1700Susan WisemanCambridgeCambridge University press2015X, 245 p.ill.23 cmLetteratura ingleseMetamorfosi820.93622Wiseman,Susan763267ITUNINAREICATUNIMARCBK9910313456103321820.936 WIS 1Bibl.2019FLFBCFLFBCWriting metamorphosis in the english Renaissance1548352UNINA02975nam 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