LEADER 00987nam a22002531i 4500 001 991002311859707536 005 20030724122406.0 008 030925s1910 it |||||||||||||||||ita 035 $ab12271342-39ule_inst 035 $aARCHE-031891$9ExL 040 $aBiblioteca Interfacoltà$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a722 100 1 $aMicalella, Mario Antimo$0452709 245 10$aDue nuovi Dolmens scoperti a Giurdignano /$cM. A. Micalella 260 $aLecce :$bTip. Coop. Dante Alighieri,$c1910 300 $a1 v. ;$c24 cm 500 $aEstr. da: Corriere Meridionale, a. XXI, n. 5(1910) 650 4$aDolmen$xSalento 907 $a.b12271342$b02-04-14$c08-10-03 912 $a991002311859707536 945 $aLE002 Misc. I F 14/10$g1$iLE002-17172$lle002$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i12662975$z08-10-03 996 $aDue nuovi Dolmens scoperti a Giurdignano$9153065 997 $aUNISALENTO 998 $ale002$b08-10-03$cm$da $e-$fita$git $h0$i1 LEADER 02975nam 2200769z- 450 001 9910576883403321 005 20220621 035 $a(CKB)5720000000008340 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84505 035 $a(oapen)doab84505 035 $a(EXLCZ)995720000000008340 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvanced Methods of Power Load Forecasting 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (128 p.) 311 08$a3-0365-4218-3 311 08$a3-0365-4217-5 330 $aThis 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. 606 $aPhysics$2bicssc 606 $aResearch and information: general$2bicssc 610 $aArtificial Neural Network 610 $aattention 610 $abidirectional long short-term memory 610 $aCNN 610 $adeep learning 610 $adeep neural network 610 $ademand 610 $aDIMS 610 $aencoder decoder 610 $aforecast 610 $agalvanizing 610 $aHolt-Winters model 610 $airregular 610 $aload 610 $along-term forecasting 610 $aLSTM 610 $amachine learning 610 $amulti-layer stacked 610 $amultiple seasonality 610 $aneural network 610 $aonline training 610 $aparameters tuning 610 $apeak load 610 $apower system 610 $aprophet model 610 $aProphet model 610 $arecurrent neural network 610 $ashort-term electrical load forecasting 610 $ashort-term load forecast 610 $ashort-term load forecasting 610 $astatistical analysis 610 $atime series 615 7$aPhysics 615 7$aResearch and information: general 700 $aGarcía-Díaz$b J. Carlos$4edt$01323492 702 $aTrull$b Óscar$4edt 702 $aGarcía-Díaz$b J. Carlos$4oth 702 $aTrull$b Óscar$4oth 906 $aBOOK 912 $a9910576883403321 996 $aAdvanced Methods of Power Load Forecasting$93035623 997 $aUNINA