Short-term Memory Solar Energy Forecasting
Short-term Memory Solar Energy Forecasting at University of Illinois Adele Kuzmiakova ”akuzmiakova”, Gael Colas ”colasg”, Alex McKeehan ”mckeehan” December 2017 Abstract: Climate change and energy crisis have motivated the use and development of solar power generation. Since solar power generation is highly intermittent and dependent on local weather characteristics, we apply a variety of linear and non-linear ensemble machine learning models to predict short-term solar energy output at the University of Illinois campus. Implemented models include weighted linear regression with and without dimension reduction, boosting regression trees, and artificial neural networks with and without vanishing temporal gradient. Additionally, we apply a variety of variable selection techniques, which suggest that air temperature, relative humidity, and dew point are the most informative weather parameters for predicting the solar energy generation at the Illinois power plant. Findings from our pape