Project Team
Students
Tanner Marchant
Energy Engineering
University Park, York
Faculty Mentors
Renee Obringer
University Park
Earth and Mineral Sciences
Project
Project Video
Project Abstract
Reliable solar power forecasting is an essential prerequisite to integrate solar systems into a stable grid. This project seeks to identify the main climate variables that impact solar energy generation, and accurately predict power output based on those variables. Predictions are made using machine learning with a random forest model based on hourly data from 2016 to 2020. After running the models, the main three variables found to affect solar energy generation were humidity, air temperature, and precipitation. Power forecasting systems can enable us to provide as much energy as we need without excess or deficit.
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