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Day-ahead natural gas demand forecasting in hourly resolution

dc.creatorPanapakidis I.P., Polychronidis V., Bargiotas D.en
dc.date.accessioned2023-01-31T09:41:38Z
dc.date.available2023-01-31T09:41:38Z
dc.date.issued2021
dc.identifier10.1109/UPEC50034.2021.9548273
dc.identifier.isbn9781665443890
dc.identifier.urihttp://hdl.handle.net/11615/77480
dc.description.abstractNatural Gas (NG) demand forecasting is a research topic that starts to gather the attention of scholars, research institutions, utilities, retailers and other interested parties. Accurate predictions of future needs for NG can aid on the optimal management of NG resources. This manuscript examines the problem of day-ahead Natural Gas (NG) demand forecasting in hourly resolution. Various models of different type are trained and applied using data that correspond to the demand of a large region including urban, sub-urban and industrial loads. A series of scenarios are formed in order to investigate the influence of input selection on the day-ahead forecasting problem. © 2021 IEEE.en
dc.language.isoenen
dc.source2021 56th International Universities Power Engineering Conference: Powering Net Zero Emissions, UPEC 2021 - Proceedingsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116642121&doi=10.1109%2fUPEC50034.2021.9548273&partnerID=40&md5=fdaf949cf3548bbd07dfb6834a646a1e
dc.subjectForecastingen
dc.subjectMachine learningen
dc.subjectNeural networksen
dc.subjectAccurate predictionen
dc.subjectDay-aheaden
dc.subjectDemand forecastingen
dc.subjectNatural gas demanden
dc.subjectNeural-networksen
dc.subjectOptimal managementen
dc.subjectPoweren
dc.subjectPower systemen
dc.subjectResearch institutionsen
dc.subjectResearch topicsen
dc.subjectNatural gasen
dc.subjectInstitute of Electrical and Electronics Engineers Inc.en
dc.titleDay-ahead natural gas demand forecasting in hourly resolutionen
dc.typeconferenceItemen


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