• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Graduate Student Research
    • ETD: Electronic Theses and Dissertations
    • Master's Theses
    • View Item
    •   Shocker Open Access Repository Home
    • Graduate Student Research
    • ETD: Electronic Theses and Dissertations
    • Master's Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A hybrid approach to forecasting wind power using Artificial Neural Networks and Numeric Weather Prediction

    View/Open
    t11079_Pfeifer.pdf (572.8Kb)
    Date
    2011-07
    Author
    Pfeifer, Mark B.
    Advisor
    Jewell, Ward T.
    Metadata
    Show full item record
    Abstract
    A methodology to forecast wind power production 24 hours ahead is developed using a hybrid approach of an artificial neural network (ANN) and numerical weather prediction (NWP). The methodology is simple and designed to be applicable to any wind farm on the globe, using publicly available NWP data and basic historical power production data from wind farm. Notably, no historical wind data from on-farm sensors is required as the 0 hour forecast data is used to train the ANN. The results are encouraging, with a root-mean-square-error of 0.2267 for a 24 hour ahead forecast, corresponding to a forecast error standard deviation of 0.23 per unit.
    Description
    Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science.
    URI
    http://hdl.handle.net/10057/5031
    Collections
    • CE Theses and Dissertations
    • EECS Theses and Dissertations
    • Master's Theses

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV