Improving power production by better recognizing power quality issue events with machine learning
Solar energy offers a green energy future both for Canada and the world. To best collect this energy, solar farms, collections of solar panels, are often distributed to ensure efficient local collection. Weather is often a challenge for production; however, the failure of components can also adversely affect this as well. These failures can be difficult to track and predict; in this project, we propose to develop tools to help operators expect events that could lead to power losses and improve solar energy harvesting using both standard analytical tools and machine learning. This will lead to improved economic benefits for Canada and production efficiency improvements.