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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 1948 the steady wind the varying wind (a) Time responses of the wind turbine. the steady wind the varying wind (b) AC power v.s. wind speeds. voltage current power (c) Scatter diagrams of the DC power v.s. AC power 7LPH 6 )DQ 7HVW 3ZLQG : 3FRQWUROOHU : 7LPH 6 :LQGVSHHG P V 7LPH 6 )DQ 7HVW 3ZLQG : 3FRQWUROOHU : 7LPH 6 :LQGVSHHG P V :LQG6SHHG P V :LQG VSHHG 96 $& 3RZHU 7KHRUHP 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW :LQG6SHHG P V :LQG VSHHG 96 $& 3RZHU 7KHRUHP 8QVWDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW 9DF 9 $& 9ROWDJH 96 '& 9ROWDJH 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW 9DF 9 $& 9ROWDJH 96 '& 9ROWDJH 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW $& 3RZHU : $& 3RZHU 96 '& 3RZHU 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW 6WDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW 8QVWDEOH ZLQG H[SHULPHQW GDWD OLQHDU Figure3.Experimental responsesof thewindturbine. 2.3. TheHydrogenElectrolysisModel Thehydrogenelectrolyzer transfers redundantenergy, i.e., theextra-renewableenergywhenthe batterySOCisnear100%, intohydrogenwhenthepowersupply isgreater thanthe load. Thestored hydrogen is thenconverted intoelectricitybyaPEMFCwhenthe loaddemandexceeds thepower supply. Therefore, a theoretical model can be built to estimate hydrogen production based on redundant renewable energy. Ahydrogen electrolyzer utilizes this redundant energy toproduce hydrogen. Thehydrogenelectrolyzation system is shown inFigure 4. It consists of a commercial hydrogen electrolyzer, HGL-1000U, with a rating energy consumption of 400W and hydrogen 198
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Titel
Short-Term Load Forecasting by Artificial Intelligent Technologies
Autoren
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Herausgeber
MDPI
Ort
Basel
Datum
2019
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-03897-583-0
Abmessungen
17.0 x 24.4 cm
Seiten
448
Schlagwörter
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Kategorie
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Short-Term Load Forecasting by Artificial Intelligent Technologies