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Solar Power Forecasting

Principal Investigator:

Dr. Zeyar Aung, Associate Professor of Electrical Engineering and Computer Science, Masdar Institute


Probabilistic forecasts account for the uncertainty in the prediction helping the decision makers take optimal decisions. With the emergence of renewable technologies and the uncertainties involved with the power generated through them, probabilistic forecasts can come to the rescue. Wind power is a mature technology and is in place for decades now, various probabilistic forecasting techniques are used here. On the other hand solar power is an emerging technology and as the technology matures there will be a need for forecasting the power generated days ahead. In this study, we utilize some of the probabilistic forecasting techniques in the field of solar power forecasting. An ensemble approach is used with different machine learning algorithms and different initial settings assuming normal distribution for the forecasts. It is observed that having multiple models with different initial settings gives exceedingly better results when compared to individual models. Getting accurate forecasts will be of great help where the large scale solar farms are integrated into the power grid.