A submission for the National High School Big Data Challenge (top 20/566).
To mitigate the effects of global climate change caused by fossil fuel emissions, Canada needs to reach net-zero emissions as soon as possible. But for a country that relies heavily on nonrenewable resources to heat homes, fuel transportation and support industry, the renewable alternative must be reliable, efficient, and effective. One of the front-runners in sustainable energy solutions is solar power, and in a country as vast as Canada, it is sure to yield promising results. Our team analyzed the photovoltaic (PV) potential of different geographical sites across the country using data from the Canadian Weather Energy and Engineering Datasets (CWEEDS). Using K-means clustering, an unsupervised machine learning model, we placed all 564 locations into 5 clusters and then predicted the PV potential for each cluster using a range of imminence and radiation variables. Through plotting our results on scatter graphs, we concluded revealed that PV potential in most of Canada is much higher than the world average (4.11-6.96 kW h/m2 ). Furthermore, the province of Alberta - known for its tar sands and oil production - has the highest PV potential in the country, so the province has the potential to become the leader in solar energy production in Canada. These findings can aid provincial and municipal governments in optimizing their shift towards solar power and other renewable energy sources to maximize output. By identifying solar power as a strong alternative to fossil fuels, administrations can now start working towards setting up solar farms in places where they would optimally serve Canadians and take the first step to decrease our national carbon footprint.