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Supporting code for wind turbine acceptance rates analysis conducted within Great Britain. Contains conference papers presented at ECOS2017 and SET2017.

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Conference Papers

This repository contains the papers and supporting analysis which were completed within my PhD.

Presented at ECOS 2017 - The 30th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.

This study has explored whether the planning success of proposed wind turbine projects can be predicted using a range of geospatial parameters based on Great Britain as a case study. Logistic regression is used to assess the relationship between appropriate variables and planning outcome.

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Presented at SET2017 - The 16th International Conference on Sustainabe Energy Technologies

Building upon the analysis conducted in the Wind Statistical Analysis, a geospatial information system tool is designed to locate suitable locations for wind turbines.

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Supporting code for wind turbine acceptance rates analysis conducted within Great Britain. Contains conference papers presented at ECOS2017 and SET2017.

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