Poster Abstract: Combining Multiple Forecast for Improved Day Ahead Prediction of Wind Power Generation

Wind, a major alternative source of energy, provides dynamic output due to frequent weather changes, which introduces one of the biggest challenges in integrating it with the existing power system. Commercial wind power forecasters vary in their prediction accuracies both across the wind farms and for different time periods within a farm. Therefore, wind power generators (WPGs) employ multiple such forecasters and heuristically choose day-ahead-prediction from one of them (baseline model). In this work, we combine multiple forecasters to generate a superforecast for the day-ahead-prediction which is, expected to be better than individual forecasters in terms of penalty - the cost a WPG has to pay for inaccurate predictions. Performance evaluation using 6 months of SCADA and forecaster data, from a WPG, of a wind farm located in India, shows that superforecast reduced the penalty by 7% and 13% when compared with the least penalised forecaster for each month and the baseline model.