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References
1. Fan,S.;Hyndman,R.J. Short-termloadforecastingbasedonasemi-parametricadditivemodel. IEEETrans.
PowerSyst. 2012,27, 134–141. [CrossRef]
2. Du,P.;Wang, J.;Yang,W.;Niu,T.Multi-stepaheadforecasting inelectricalpowersystemusingahybrid
forecastingsystem.Renew. Energy2018,122, 533–550. [CrossRef]
3. Shrivastava,N.A.;Khosravi,A.;Panigrahi,B.K.PredictionIntervalEstimationofElectricityPricesusing
PSOtunedSupportVectorMachines. IEEETrans. Ind. Inform. 2015,11. [CrossRef]
4. Hagan,M.T.;Behr,S.M.TheTimeSeriesApproachtoShortTermLoadForecasting. IEEETrans. PowerSyst.
1987,2, 785–791. [CrossRef]
5. Papalexopoulos,A.D.;Hesterberg,T.C.Aregression-basedapproachtoshort-termsystemloadforecasting.
IEEETrans. PowerSyst. 1990,5, 1535–1547. [CrossRef]
6. Christiaanse,W.R.Short-TermLoadForecastingUsingGeneralExponentialSmoothing. IEEETrans. Power
Appar. Syst. 1971,PAS-90, 900–911. [CrossRef]
7. Al-Hamadi,H.M.;Soliman,S.A.Short-termelectric loadforecastingbasedonKalmanfilteringalgorithm
withmovingwindowweatherandloadmodel.Electr. PowerSyst. Res. 2004,68, 47–59. [CrossRef]
8. Metaxiotis, K.; Kagiannas,A.; Askounis,D.; Psarras, J.Artificial intelligence in short termelectric load
forecasting: A state-of-the-art survey for the researcher. Energy Convers. Manag. 2003, 44, 1525–1534.
[CrossRef]
9. Yoo, H.; Pimmel, R.L. Short term load forecasting using a self-supervised adaptive neural network.
IEEETrans. PowerSyst. 1999,14, 779–784. [CrossRef]
10. Ho,K.L.;Hsu,Y.Y.;Chen,C.F.; Lee,T.E.; Liang,C.C.; Lai,T.S.;Chen,K.K.Short termload forecastingof
taiwanpowersystemusingaknowledge-basedexpert system. IEEETrans. PowerSyst. 1990,5, 1214–1221.
[CrossRef]
11. Mohandes,M.Supportvectormachines forshort-termelectrical loadforecasting. Int. J.EnergyRes. 2002,
26, 335–345. [CrossRef]
12. Hong,W.C.Electric load forecastingbyseasonal recurrentSVR(supportvector regression)withchaotic
artificialbeecolonyalgorithm.Energy2011,36, 5568–5578. [CrossRef]
13. Liu,Z.;Li,W.;Sun,W.Anovelmethodofshort-termloadforecastingbasedonmultiwavelet transformand
multipleneuralnetworks.NeuralComput.Appl. 2013,22, 271–277. [CrossRef]
14. Fan,G.F.; Peng, L.L.; Hong,W.C.; Sun, F. Electric load forecasting by the SVRmodelwith differential
empiricalmodedecompositionandautoregression.Neurocomputing2016,173, 958–970. [CrossRef]
15. AlRashidi,M.R.;EL-Naggar,K.M.Longtermelectric loadforecastingbasedonparticle swarmoptimization.
Appl. Energy2010,87, 320–326. [CrossRef]
16. Wang, J.;Li,L.;Niu,D.;Tan,Z.Anannual loadforecastingmodelbasedonsupportvector regressionwith
differentialevolutionalgorithm.Appl. Energy2012,94, 65–70. [CrossRef]
17. Ghayekhloo, M.; Menhaj, M.B.; Ghofrani, M. A hybrid short-term load forecasting with a new data
preprocessingframework.Electr. PowerSyst. Res. 2015,119, 138–148. [CrossRef]
18. Zhang,X.;Wang, J.Anoveldecomposition-ensemblemodel for forecastingshort-termload-timeserieswith
multipleseasonalpatterns.Appl. SoftComput. J.2018,65, 478–494. [CrossRef]
19. Tian,C.;Hao,Y.ANovelNonlinearCombinedForecastingSystemforShort-TermLoadForecasting.Energies
2018,11, 714.
20. Khotanzad,A.;Hwang,R.C.;Abaye,A.;Maratukulam,D.AnAdaptiveModularArtificialNeuralNetwork
Hourly Load Forecaster and its Implementation at Electric Utilities. IEEE Trans. Power Syst. 1995,
10, 1716–1722. [CrossRef]
21. Bishop,C.M.Neuralnetworks forpatternrecognition. J.Am. Stat.Assoc. 1995,92, 482. [CrossRef]
22. Hwang, J.T.G.; Ding,A.A.Prediction Intervals forArtificialNeuralNetworks. J.Am. Stat. Assoc. 1997,
92, 748–757. [CrossRef]
23. Heskes,T.Practical confidenceandprediction intervals.Adv.Neural Inf. Process. Syst. 1997,9, 176–182.
24. Nix, D.A.; Weigend, A.S. Estimating the mean and variance of the target probability distribution.
InProceedingsof the1994 IEEE InternationalConferenceonNeuralNetworks (ICNN’94),Orlando, FL,
USA,28 June–2July1994;Volume1,pp.55–60.
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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