An artificial neural network approach for short-term wind speed forecast

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dc.contributor.author Datta, Pallab Kumar
dc.date.accessioned 2018-05-07T13:34:00Z
dc.date.available 2018-05-07T13:34:00Z
dc.date.issued 2018-05-01 en_US
dc.identifier.uri http://hdl.handle.net/2097/38945
dc.description.abstract Electricity generation capacity from different renewable sources has been significantly growing worldwide in recent years, specially wind power. Fast dispatch of wind power provides flexibility for spinning reserve. However, wind is intermittent in nature. Thus, stable grid operations and energy management are becoming more challenging with the increasing penetration of wind in power systems. Efficient forecast methods can help the scenario. Many wind forecast models have been developed over the years. Highly effective models with the combination of numerical weather prediction and statistical models also exist at present. This study intends to develop a model to forecast hourly wind speed using an artificial neural network (ANN) approach for effective and fast operation with minimum data. The procedure is outlined in this work and the performance of the ANN model is compared with the persistence forecast model. en_US
dc.language.iso en_US en_US
dc.subject Artificial neural network en_US
dc.subject Wind speed forecast en_US
dc.title An artificial neural network approach for short-term wind speed forecast en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Electrical and Computer Engineering en_US
dc.description.advisor Anil Pahwa en_US
dc.date.published 2018 en_US
dc.date.graduationmonth August en_US


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