Using a Spatial Regression Model to Project Spatial Patterns of Photovoltaic Farms in Ontario
Providing environmentally sustainable energy is becoming a greater concern due to the urgency to reduce fossil fuel emissions. Solar photovoltaic (PV) technology is considered one of the leading renewable energy sources as it converts sunlight into electricity. There is a great interest in building large-scale photovoltaic farms, but it is a complex process as it requires incorporating a number of environmental, social, and economic factors to determine the location of new sites. The aim of this research was to develop a GIS-based regression model that used statistical analysis to determine the most spatially significant factors affecting the siting of photovoltaic farms in Ontario. The factors taken into consideration were land use, land quality, slope, aspect, drainage, proximity to water bodies, transportation access, access to existing transformer stations, and population density. The regression model was run on 65% of the PV farms in Ontario and later validated with the remaining 35%. It was found that distance from the farms to the nearest transformer station was the most significant factor on an individual basis. When the model was ran using multiple factors, it was found that land use and aspect had the highest significance with farm sites. Overall the regression model was successful in determining which factors influence the siting of PV farms the most. Some limitations include not accounting for varying solar insolation, as well as some of the data layers not extending to the whole province of Ontario. This research can be used for future sitings of PV farms in Ontario, and has the potential to be adapted for applications in different regions with similar spatial features to Ontario.
Acknowledgements: We would like to thank our instructor Adam Bonnycastle and teaching assistants Melanie Chabot and Nabil Allataifeh for their assistance and guidance throughout this project. We would also like to give a special thank you to Kirby Calvert for allowing us to work on this project and for his guidance throughout the process of the project.