Ensemble of artificial neural networks for seasonal forecasting of wind speed in eastern Canada
Author: Pia Milena Leminski
Year: 2024
Supervisors: Taha BMJ Ouarda, Ármann Gylfason
Abstract:
Efficient harnessing of wind energy resources, including advances in weather and seasonal forecasting and climate projections, is imperative for the sustainable progress of wind power generation. While temperature and precipitation data receive considerable attention in interannual variability and seasonal forecasting studies, there is a notable gap in exploring correlations between climate indices and wind speeds. This paper proposes the use of an ensemble of artificial neural networks to forecast wind speeds based on climate oscillation indices and assesses its performance. An initial examination indicates a correlation signal between climate indices and wind speeds of ERA5 for the selected case study in eastern Canada. Forecasts are made for the season April-May-June and based on the most correlated climate indices of previous seasons.
A pointwise forecast is conducted with a 20 member ensemble, which is verified by leave-on-out cross-validation. The results obtained are analyzed in terms of root mean squared error, bias, and skill score and show a competitive performance with state-of-the-art numerical wind predictions from SEAS5, outperforming them in several regions. A relatively basic model with a single unit in the hidden layer and regularization rate of 10−2 provides promising results, especially in areas with a higher number of indices considered. This study adds to global efforts to enable more accurate forecasting by introducing a novel approach.