Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning

Solar energy production using photovoltaic (PV) systems is increasingly popular as a source of renewable energy for numerous applications. However, there is a main challenge with solar energy, namely, the unpredictability of its energy output. Therefore, accurate short-term predicting of the power output for PV systems is essential for effective decision making in power grid management. To this end, this paper focuses on training selected machine learning models, both traditional regression models and deep recurrent neural networks, to accurately predict solar energy output on meteorological time-series data from the Alice Springs solar farm in Australia. These machine learning models include linear regression, gated recurrent unit, recurrent neural network, long short-term memory, and random forest regression. The results of these tests showed that simple ensemble methods can outperform powerful single models and that hyperparameter tuning can greatly improve the performance of a model