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Energies2018,11, 1561
mixing, theEEMD[46],defines the“true”modesas theaverageof thecorrespondingIMFsobtained
from an ensemble of the original signal plus different realizations of finite variancewhite noise.
But incompletion of decomposition still exists, and thenumber ofmodeswill bedifferent due to
thenoise added. Taking these short comes into account,CEEMDANisproposed. Thedetails are
describedas follows: letEk(·)betheoperatorwhichproduces thekthmodeobtainedbyEMDandw(i)
bearealizationofwhitenoisewithN (0, 1).AndthentheprocessofCEEMDANcanbeexpressedas
several stages:
1ststep. For every i = 1,. . . , I decompose each x(i) = x+β0w(i) byEMD, until the firstmode is
extractedandcompute d˜1 by:
d˜1= 1
I I
∑
i=1 di1= d1 (1)
2ndstep.At thefirst stage (k=1)calculate thefirst residueas r1= x− d˜1.
3rdstep.Obtain the first mode of r1+ β1E1(wi) , i=1,. . . I, by EMD and define the second
CEEMDANmodeas:
d˜2= 1
I I
∑
i=1 E1(r1+β1E1(w(i))) (2)
4thstep.Fork=2,. . .K calculate thekthresidue:
rk= r(k−1)− d˜k (3)
5thstep.Obtain thefirstmodeof rk+βkEk(w(i)) , i=1,. . . , I, byEMDuntil define the (k+1)th
CEEMDANmodeas:
d˜(k+1) = 1
I I
∑
i=1 E1(rk+βkEk(w(i))) (4)
6thstep.Goto4thstepfor thenextk.
Iterate thesteps4 to6until theobtainedresiduecannotbe furtherdecomposedbyEMD,either
because it satisfies IMFconditionsorbecause ithas less than three local extremums. Observe that,
byconstructionofCEEMDAN,thefinal residuesatisfies:
rK= x− K
∑
k=1 d˜k (5)
withKbeingthe totalnumberofmodes. Therefore, thesignalof interestxcanbeexpressedas:
x= K
∑
k=1 d˜k+rk (6)
whichensures thecompletenesspropertyof theproposeddecompositionandthusprovidinganexact
reconstructionof theoriginaldata. Thefinalnumberofmodes isdeterminedonlybythedataandthe
stoppingcriterion. Thecoefficientsβk= εkstd(rk)allowtheselectionof theSNRateachstage.
TheCEEMDANmethodcanaddalimitednumberofself-usewhitenoisesateachstage,whichcan
achieve almost zero reconstruction error with fewer average times. Therefore, CEEMDAN can
overcomethe“mode-mixing”phenomenonexisting inEMD,andcanalsosolve the incompletenessof
EEMDdecompositionandreduce thecomputationalefficiencybyreducingthereconstructionerrorby
increasingthenumberof integrations.
292
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