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Energies2018,11, 1009
movements, it isalsocapable tofindout theglobaloptimum, i.e., it couldensure that thesystemwill
notbe trappedina localoptimum[41].
2.2.3. ImplementationStepsofCCSAlgorithm
The procedure of the hybridCCS algorithmwith an SVRmodel is illustrated as followings.
Therelevantflowchart is showninFigure1.
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Figure1.Chaoticcuckoosearchalgorithmflowchart.
Step1: Initialization.
The locations of random n nests for the three parameters of an SVR model as
x(i)k,j = [
x(i)k,1,x (i)
k,2, . . . ,x (i)
k,n ]T
, k=C, σ, ε; i represents the iterationnumber; j represents thenumberof
28
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
- Informatik