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dc.contributor.authorMishra S.en_US
dc.contributor.authorSharma A.en_US
dc.contributor.authorPanda G.en_US
dc.date.accessioned2020-01-13T05:19:27Z-
dc.date.available2020-01-13T05:19:27Z-
dc.date.issued2011-
dc.identifier.citation3en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ICEAS.2011.6147151-
dc.identifier.urihttp://10.10.32.48:8080/jspui/handle/2008/84-
dc.description.abstractDue to growing share of wind power in world's energy consumption, forecasting of the wind power becomes essential for proper utilization. This paper proposes short term wind power forecasting model using complex wavelet transform and neural network. The past wind power values are transferred into real and complex signal; which are further transferred in Wavelet domain signal. These signals are used to predict next hour wind power using neural network. This approach is tested using data from Alberta wind farm. � 2011 IEEE.en_US
dc.language.isoenen_US
dc.subjectComplex waveleten_US
dc.subjectNeural networken_US
dc.subjectWind Power forecastingen_US
dc.titleWind power forecasting model using complex wavelet theoryen_US
dc.typeConference Paperen_US
Appears in Collections:Research Publications

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