Seite - 62 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
Bild der Seite - 62 -
Text der Seite - 62 -
Energies2019,12, 164
Author Contributions: Conceptualization, A.A.; Formal analysis, A.M. and M.A.; Investigation, A.A.;
Methodology,A.A.andN.J.; Software,A.A.;Supervision,N.J.;Validation,N.J.,A.M.andM.A.;Writing—original
draft,A.A.;Writing—review&editing,N.J. andZ.A.K.
Funding:This researchreceivednoexternal funding.
Conflictsof Interest:Theauthorsdeclarenoconflictsof interest.
Nomenclature
SG Smartgrid
DAL Day-aheadload
DALF Day-aheadloadforecast(ing)
AN Artificialneuron
ANN Artificialneuralnetwork
MARA Multivariateautoregressivealgorithm
ARMA Autoregressiveandmovingaverage
EDE Enhanceddifferentialevolutionalgorithm
mEDE ModifiedversionofEDEalgorithm
NIST National instituteofstandardsandtechnology
MSE Minimumsquareerror
P Historical loaddatamatrix
TDP Historicaldewpoint temperaturedatamatrix
TDB Historicalboilingpoint temperaturedatamatrix
DTYP Historicaldewpoint temperaturedatamatrix
phm,dn Loadvalueatmthhourof thenthday
pcimax LocalmaximaforeachcolumnofP
Pnrm LocallynormalizedP
TDP,nrm LocallynormalizedTDP
TDB,nrm LocallynormalizedTDB
MI(K,G) Relativemutual informationbetweeninputKandtargetG
pr(K,G) JointprobabilitybetweenKandG
pr(K) IndividualprobabilityofK
Sf Selectedfeatures
ST Trainingsamples
SV Validationsamples
MAPE Meanabsolutepercentageerror
pa Actual load
pf Forecasted load
Ith Irrelevancythresholdvalue
Rth Redundancythresholdvalue
y ′t
i,j jth trialvectory ′
for ith individual ingeneration t
xti,j jthparentvectorx for ith individual ingeneration t
uti,j jthmutantvectoru for ith individual ingeneration t
yti,j jthoffspringvectory for ith individual ingeneration t
rnd Randomnumber
FFN(.) Fitness function
EF Forecasterror
References
1. Gelazanskas,L.;Gamage,K.A.Demandsidemanagement insmartgrid:Areviewandproposals for future
direction.Sustain. CitiesSoc. 2014,11, 22–30. [CrossRef]
2. Yan,Y.;Qian,Y.; Sharif,H.;Tipper,D.ASurveyonSmartGridCommunicationInfrastructures:Motivations,
RequirementsandChallenges. IEEECommun. Surv. Tutor. 2013,15, 5–20. [CrossRef]
62
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