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Wind and Structures Volume 15, Number 1, January 2012 , pages 43-64 DOI: https://doi.org/10.12989/was.2012.15.1.043 |
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A neural network shelter model for small wind turbine siting near single obstacles |
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Andrew William Brunskill and William David Lubitz
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| 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. A neural networkbased model has been developed which predicts mean wind speed and turbulence intensity at points in an obstacle\'s region of influence, relative to unsheltered conditions. The neural network was trained using measurements collected in the wakes of 18 scale building models exposed to a simulated rural atmospheric boundary layer in a wind tunnel. The model obstacles covered a range of heights, widths, depths, and roof pitches typical of rural buildings. A field experiment was conducted using three unique full scale obstacles to validate model predictions and wind tunnel measurements. The accuracy of the neural network model varies with the quantity predicted and position in the obstacle wake. In general, predictions of mean velocity deficit in the far wake region are most accurate. The overall estimated mean uncertainties associated with model predictions of normalized mean wind speed and turbulence intensity are 4.9% and 12.8%, respectively. | ||
| Key Words | ||
| wind tunnel; small wind turbine; wind energy; micrositing; wake prediction; anemometer; sheltering; neural network. | ||
| Address | ||
| Andrew William Brunskill and William David Lubitz : University of Guelph, School of Engineering. 50 Stone Road East, Guelph, Ontario, Canada. N1G 2W1 | ||