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[TODO -- redo intro] Since variable generation and electricity demand both fluctuate, they must be forecast ahead of time to inform real-time electricity scheduling and longer-term system planning. Better short-term forecasts can allow system operators to reduce their reliance on polluting standby plants and to proactively manage increasing amounts of variable sources. Better long-term forecasts can help system operators (and investors) determine where and when power plants should be built. While many system operators today use basic forecasting techniques, forecasts will need to become increasingly accurate, span multiple horizons in time and space, and better quantify uncertainty to support these use cases. Machine learning has commonly been used on all these fronts.
== Background Readings ==
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* '''"Current methods and advances in forecasting of wind power generation" (2012)'''<ref>{{Cite journal|last=Foley|first=Aoife M.|last2=Leahy|first2=Paul G.|last3=Marvuglia|first3=Antonino|last4=McKeogh|first4=Eamon J.|date=2012-01|title=Current methods and advances in forecasting of wind power generation|url=http://dx.doi.org/10.1016/j.renene.2011.05.033|journal=Renewable Energy|volume=37|issue=1|pages=1–8|doi=10.1016/j.renene.2011.05.033|issn=0960-1481}}</ref>: A review of work in wind power forecasting.
* '''"A review on the forecasting of wind speed and generated power"''' '''(2009)'''<ref>{{Cite journal|last=Lei|first=Ma|last2=Shiyan|first2=Luan|last3=Chuanwen|first3=Jiang|last4=Hongling|first4=Liu|last5=Yan|first5=Zhang|date=2009-05|title=A review on the forecasting of wind speed and generated power|url=http://dx.doi.org/10.1016/j.rser.2008.02.002|journal=Renewable and Sustainable Energy Reviews|volume=13|issue=4|pages=915–920|doi=10.1016/j.rser.2008.02.002|issn=1364-0321}}</ref>: A review of work in wind speed and wind power forecasting.
== Community ==
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