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Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model

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  • Wimhurst, Joshua J.
  • Greene, J. Scott
  • Koch, Jennifer

Abstract

Guiding wind energy sector growth through suitability analysis is a growing priority. We present in this work a logistic regression model that predicts suitable sites for state-level and nationwide wind energy development in the United States. The model's aggregation of publicly available data to 20 different grid cell resolutions, along with four predictor configurations, allows end-users to investigate commercial wind farm site suitability for their region, project size, and predictors of interest. Model performance maximizes at higher grid cell resolutions and when using a complete and/or refined predictor set. Validation of the model's performance against existing commercial wind farm locations demonstrates its ability to consistently diagnose over 75% of grid cell states correctly. As such, high suitability grid cells that currently lack wind farms could represent candidate locations for wind farm construction. This model and its aggregated datasets can be applied in other suitability analysis contexts, particularly solar energy development.

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  • Wimhurst, Joshua J. & Greene, J. Scott & Koch, Jennifer, 2023. "Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923012448
    DOI: 10.1016/j.apenergy.2023.121880
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