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Energies2018,11, 1449
Toobtain thesolution, theLagrangefunctioncanbeestablishedasEquation(8).
L(w,b,ξi,αi)= 1
2 wTw+ 1
2 γ N
∑
i=1 ξ2i − N
∑
i=1 αi [
wTϕ(xi)+b+ξi−yi ]
(8)
whereαi is theLagrangemultipliers. Takethederivativesofeachvariable in the functionandmake
themequalzero: ⎧⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎩ ∂L
∂w=0→w= N
∑
i=1 αiϕ(xi)
∂L
∂b =0→ N
∑
i=1 αi=0
∂L
∂ξ =0→αi=γξi
∂L
∂α =0→wTϕ(xi)+b+ξi−yi=0 (9)
Eliminatewaswellasξi andtransformit into the
followingproblem:[
0 eTn
en Ω+γ−1 · I ]
× [
b
a ]
= [
0
y ]
(10)
where
Ω=ϕT(xi)ϕ(xi) (11)
en=[1,1,...,1] T (12)
α=[α1,α2,...,αn] (13)
y=[y1,y2,...,yn] T (14)
Thesolutioncanbeobtainedbasedonthe linearequationsabove:
y(x)= N
∑
i=1 αiK(xi,x)+b (15)
whereK(xi,x) is thekernel function thatmeetsMercer’s condition. Theradialbasis function (RBF)
is employedas thekernel functionhereon thebasis of itswide convergence regionandextensive
applicationscope,asshowninEquation(16).
K(xi,x)= exp {
−‖x−xi‖2/2σ2 }
(16)
whereσ2 represents thekernelparameter that reflects thecharacteristicof trainingsamplesandhas
influenceongeneralizationabilityof the technique.
Aswe can see, the performance improvement of LSSVMmodel is greatly dependent on the
appropriate settingof the followingparameters: regularizationparameterγandkernelparameter
σ2 [33].
3.3.WolfPackAlgorithm
Inconsiderationof theblindnessofmanualselection inLSSVMmodelparameters, theoptimal
valueof regularizationparameterγandkernelparameterσ2 ofLSSVMisobtainedthroughthewolf
packalgorithm.TheWPAtechnique is inspiredbyresearchonthehuntingbehaviorsofwolves [34].
Accordingto their roles inhunting,wolvescanbedividedinto three types: headwolves, safariwolves
andferalwolves,whoworktogether tocomplete the task.Randomwalk, call toactionandsiegeare
threemainbehaviorsofwolves,whicharesimulated in theWPAmodel. Thedeterminationof the
headwolfandthereplacementof thewolfpack followthecommonrules that the“winner is theking”
and“thesurvivalof thefittest”, respectively [35].WPAis illustrated inFigure5.
325
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