Page - 266 - in Short-Term Load Forecasting by Artificial Intelligent Technologies
Image of the Page - 266 -
Text of the Page - 266 -
Energies2018,11, 1678
5. Grosswindhager,S.;Voigt,A.;Kozek,M.OnlineShort-TermForecastofSystemHeatLoadinDistrictHeating
Networks. InProceedingsof the31st InternationalSymposiumonforecasting,Prague,CzechRepublic,
26–29 June2011.
6. Nielsen,H.A.;Madsen,H.Modelling theheat consumption indistrictheatingsystemsusingagrey-box
approach. EnergyBuild. 2006,38, 63–71, doi:10.1016/j.enbuild.2005.05.002. [CrossRef]
7. Idowu, S.; Saguna, S.; Åhlund,C.; Schelén,O. Forecastingheat load for smartdistrict heating systems:
Amachine learningapproach. InProceedingsof the2014 IEEEInternationalConferenceonSmartGrid
Communications (SmartGridComm),Venice, Italy,3–6November2014.
8. Izadyar,N.;Ghadamian,H.;Ong,H.C.;Moghadam,Z.; Tong,C.W.; Shamshirband, S. Appraisal of the
supportvectormachine to forecast residentialheatingdemandfor theDistrictHeatingSystembasedon
themonthlyoverallnaturalgasconsumption. Energy2015,93, 1558–1567, doi:10.1016/j.energy.2015.10.015.
[CrossRef]
9. Kusiak,A.;Li,M.;Zhang,Z. Adata-drivenapproachforsteamloadprediction inbuildings. Appl. Energy
2010,87, 925–933, doi:10.1016/j.apenergy.2009.09.004. [CrossRef]
10. Powell,K.M.; Sriprasad,A.;Cole,W.J.; Edgar,T.F. Heating, cooling, andelectrical load forecasting fora
large-scaledistrictenergysystem. Energy2014,74, 877–885, doi:10.1016/j.energy.2014.07.064. [CrossRef]
11. Kato,K.;Sakawa,M.; Ishimaru,K.;Ushiro,S.;Shibano,T.Heat loadpredictionthroughrecurrentneural
network indistrictheatingandcoolingsystems. InProceedingsof the2008IEEEInternationalConference
onSystems,ManandCybernetics,Singapore,15–16May2008;pp. 1401–1406.
12. Nielsen,T.S.;Madsen,H. ControlofSupplyTemperature inDistrictHeatingSystems. InProceedingsof the
8th InternationalSymposiumonDistrictHeatingandCooling,Trondheim,Norway,14–16August2002.
13. Hernández,L.;Baladrón,C.;Aguiar, J.M.;Calavia,L.;Carro,B.; Sánchez-Esguevillas,A.;García,P.;Lloret, J.
Experimentalanalysisof the inputvariables’ relevance to forecastnextday’saggregatedelectricdemand
usingneuralnetworks. Energies2013,6, 2927–2948. [CrossRef]
14. Saha, S.; Moorthi, S.; Pan, H.L.; Wu, X.; Wang, J.; Nadiga, S.; Tripp, P.; Kistler, R.; Woollen, J.;
Behringer, D.; et al. TheNCEPClimate Forecast SystemReanalysis. Bull. Am. Meteorol. Soc. 2010,
91, 1015–1057, doi:10.1175/2010BAMS3001.1. [CrossRef]
15. Unden, P.; Rontu, L.; Järvinen, H.; Lynch, P.; Calvo, J.; Cats, G.; Cuxart, J.; Eerola, K.; Fortelius, C.;
Garcia-Moya, J.A.; etal. HIRLAM-5ScientificDocumentation; TechnicalReport;SwedishMeteorologicaland
Hydrological Institute:Norrkoping,Sweden,2002.
16. HolidaysinDenmark. Availableonline:www.timeanddate.com/holidays/denmark/(accessedon13June2017).
17. Crawley, D.B.; Hand, J.W.; Kummert,M.; Griffith, B.T. Contrasting the capabilities of building energy
performancesimulationprograms. Build. Environ. 2008,43, 661–673, doi:10.1016/j.buildenv.2006.10.027.
[CrossRef]
18. Dahl,M.; Brun,A.;Andresen,G.B. Decision rules for economic summer-shutdownofproductionunits
in largedistrictheatingsystems. Appl. Energy2017,208C, 1128–1138, doi:10.1016/j.apenergy.2017.09.040.
[CrossRef]
19. Alpaydin,E. Introduction toMachineLearning;MITPress:Cambridge,MA,USA,2014.
20. Bishop,C.M.PatternRecognitionandMachineLearning; Springer:NewYork,NY,USA,2006.
21. Hochreiter, S.; Schmidhuber, J. Longshort-termmemory. NeuralComput. 1997,9, 1735–1780. [CrossRef]
[PubMed]
22. Drucker, H.; Burges, C.J.; Kaufman, L.; Smola, A.J.; Vapnik, V. Support vector regressionmachines.
InAdvances inNeural InformationProcessingSystems;MITPress:Cambridge,MA,USA,1997;pp. 155–161.
23. Pedregosa,F.;Varoquaux,G.;Gramfort,A.;Michel,V.;Thirion,B.;Grisel,O.;Blondel,M.;Prettenhofer,P.;
Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 2011,
12, 2825–2830.
24. Dahl,M.;Brun,A.;Andresen,G.B. Usingensembleweatherpredictions indistrictheatingoperationand
loadforecasting. Appl. Energy2017,193, 455–465, doi:10.1016/j.apenergy.2017.02.066. [CrossRef]
25. Scott,D.W.Onoptimalanddata-basedhistograms. Biometrika1979,66, 605–610. [CrossRef]
c©2018bytheauthors. LicenseeMDPI,Basel,Switzerland. Thisarticle isanopenaccess
articledistributedunder the termsandconditionsof theCreativeCommonsAttribution
(CCBY) license (http://creativecommons.org/licenses/by/4.0/).
266
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