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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2019,12, 57 developedin[36],whichcanproducehydrogenwhenthesystemrequiresenergyfromthePEMFC. Forexample, if the systemstores20kgofNaBH4, thesafety indexes for thehousehold, laboratory, andoffice loads can be extended by 3.33, 2.10, and 2.90 days, respectively, assuming an inverter efficiencyof90%. Installing40kgofNaBH4couldguarantee6.17,3.96,and5.44daysofoperationfor thehousehold, laboratory,andoffice, respectively, in theworstcasescenario. ȱ (a)ȱTheȱhouseȱloadȱ (b)ȱTheȱlabȱloadȱ ȱ (c)ȱTheȱofficeȱloadȱ Figure9.Thereferenceplotswithsafetyconsideration. Table9.Safetyanalyses. House Lab Office Dailyaverage (kWh) 19.96 30.41 22.32 Optimalsizes (b, s,w) (23,15,0) (27,21,0) (26,17,0) LowestSOC(%) 29.99 26.04 27.18 Lowest remainingenergy(kWh) 11.03 7.83 8.97 Safety (days) 0.49 0.23 0.36 1-daysafety requirement SystemSizes (b, s,w) (33,13,0) (33,24,0) (34,17,0) SystemCost (USD/kWh) 0.8152 0.7062 0.8266 2-dayssafety requirement SystemSizes (b, s,w) (33,16,0) (40,24,0) (40,17,0) SystemCost (USD/kWh) 0.8952 0.7603 0.8735 Thechoiceofasub-optimaldesignorextraNaBH4 stockwoulddependontheestimatedextreme weatherconditionsandthepriceofNaBH4. For instance, if theexpectedextremeweatherhappens onedayduringthe61-daysimulation, the total systemcostsare increasedby$25.81,$85.70,and$48.47 for thehousehold, laboratory, andoffice, respectively, using the sub-optimal settings. Conversely, therequired extraNaBH4 to guarantee sustainability under theworst-case conditions are 3.59 kg, 8.26kg,and5.04kg, respectively,assumingan inverterefficiencyof90%.Thiswill increase thesystem 94
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Title
Short-Term Load Forecasting by Artificial Intelligent Technologies
Authors
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Editor
MDPI
Location
Basel
Date
2019
Language
English
License
CC BY 4.0
ISBN
978-3-03897-583-0
Size
17.0 x 24.4 cm
Pages
448
Keywords
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Category
Informatik
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Short-Term Load Forecasting by Artificial Intelligent Technologies