Machine Learning Prediction of Short-Term Solar PV and Wind Farm Power Generation in Ontario

Abstract

<p>This research accounts for the outcome of a machine learning algorithm project to predict short term solar and wind power in Ontario. A Long Short Term Memory (LSTM) model was developed to monitor short term power output predictions from 4 hours ahead to 6 hours ahead. This study demonstrates a unique approach of utilizing nearby publicly available meteorological data to predict renewable energy power output that could potentially be used for wind and solar farm scheduling to prevent curtailment of clean energy. The wind power and the solar power was predicted 6 and 4 hours ahead with a coefficient of correlation (R2) of 0.817 and 0.738 respectively. It has demonstrated the usage of a LSTM network as a reliable tool for the prediction of renewable energy that can be implemented to power output on systems that require accurate prediction of wind and solar power within the 4 to 6 hours. </p>