Evaluating the effectiveness of synthetic training data for day-ahead wind speed prediction in the Great Lakes

With an estimated offshore potential wind energy capacity of 575 gigawatts, the Great Lakes region is a promising area for future wind energy development. Electric utilities that use wind energy rely on accurate day-ahead wind energy forecasts, mainly informed by predicted wind speed, to account for the variability of wind energy production. Since wind turbines have not yet been developed in the Great Lakes, observational wind data is collected at very few sites. Hence, utilities may seek to use synthetic wind data to pre-train accurate day-ahead wind speed prediction models. Yet, prior studies have only utilized synthetic data in predicting sub-hourly winds speeds. We hypothesized that training long short-term memory neural networks to predict day-ahead wind speeds on synthetic wind data instead of observational wind data would increase accuracy since synthetic wind data is available over longer time spans than observational wind data. We used observational data from the Lake Michigan Wind Assessment and synthetic data from the National Offshore Wind Great Lakes dataset and found that networks trained on synthetic data had a lower mean absolute percentage error score in day-ahead wind speed prediction than networks trained on observational data. We also optimized additional parameters of the networks for both synthetic and observational network types, further improving accuracy. The availability of a wind speed prediction model trained on synthetic data will reduce reliance on historical observational data at future sites of wind energy infrastructure, allowing utilities to swiftly adapt accurate prediction methods to new sites.