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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies 2018,11, 242 • For theELM, therewere100neurons in thehidden layer,andthehardlimfunctionwaschosenas theactivationfunctionforconvertingtheoriginal features intoanotherspace. • For theSVR, thepenaltycoefficientwasset tobe80,andtheradialbasis functionwaschosenas thekernel functiontorealize thenonlinear transformationof input features. D E 7LPH RI DOO WKH VDPSOLQJ GD\V RQH XQLW PLQXWHV 7LPH RI WKH GD\ RQH XQLW PLQXWHV Figure 8. Periodicity knowledge and the residual time series of the retail store data set: (a) thedaily-periodicenergy-consumingpattern; (b) theresidual timeseries. 405
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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