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A Neural Network-Based Wake Model for Small Wind Turbine Siting near Obstacles

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dc.contributor.advisor Lubitz, William David Brunskill, Andrew 2010-05-10 2010-06-03T14:09:37Z 2010-06-03T14:09:37Z 2010-06-03T14:09:37Z
dc.description Industrial collaborators: Weather INnovations Inc., Wenvor Technologies Inc. en
dc.description.abstract Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. This thesis describes the creation of a new model which can predict the wind speed, turbulence intensity, and wind power density at any point in an obstacle’s region of influence, relative to unsheltered conditions. Artificial neural networks were used to learn the relationship between an obstacle’s characteristics and its effects on the local wind. The neural network was trained using measurements collected in the wakes of scale models exposed to a simulated atmospheric boundary layer in a wind tunnel. A field experiment was conducted to validate the wind tunnel measurements. Model predictions are most accurate in the far wake region. The estimated mean uncertainties associated with model predictions of velocity deficit, power density deficit, and turbulence intensity excess are 5.0%, 15%, and 12.8%, respectively. en
dc.description.sponsorship Ontario Centre of Excellence for Energy en
dc.language.iso en en
dc.subject Micrositing en
dc.subject Wind turbine en
dc.subject Contaminant transport near buildings en
dc.subject Anemometer Sheltering Model en
dc.subject Obstacle Wake en
dc.subject Full Scale en
dc.subject Wind Tunnel en
dc.subject Neural Network en
dc.subject Wind en
dc.title A Neural Network-Based Wake Model for Small Wind Turbine Siting near Obstacles en
dc.type Thesis en Engineering en Master of Applied Science en School of Engineering en

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