Developing a Standardized GIS Model Capable of Identifying Areas to Implement Wind Power Generation Infrastructure
This project involves the use of multi-source GIS data, suitability analysis, and spatial modeling techniques to identify the most suitable areas for improving renewable energy infrastructures, primarily focusing on wind energy, within a given region. Previous literature conducted on the subject matter shows research being done in isolated areas or states. The primary objective of this project is to develop a GIS model and scripting tool that has the capability to test any input region to find the most suitable area within the given region that would be ideal for implementing new wind power infrastructure. The success rate of the model outputs was tested against pre-existing wind power infrastructure. The criteria used for the model were derived from consultations with energy industry experts and previous studies that have investigated building new wind power infrastructure in specified regions. Both the commercial ArcGIS Pro and open-source QGIS software were tested and compared. Results indicated an overall model accuracy of about 78% in both testing sites. The distance to transmission lines was found to be the most important factor affecting model performance. The Python scripting tool developed from the model however aims to be a universal tool that can be used in any region to find the most suitable areas for installing new wind power generation infrastructure.
Date of Degree
Master of Science (MS)
Geographic Information Science
Humanities and Social Sciences
De Silva, N. V. (2023). Developing a Standardized GIS Model Capable of Identifying Areas to Implement Wind Power Generation Infrastructure [Master’s alternative plan paper, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1274/
Geographic Information Sciences Commons, Physical and Environmental Geography Commons, Power and Energy Commons, Spatial Science Commons