Evaluation, Calibration, and Application of Probabilistic 100-Meter Wind Speed Forecasts Produced by the WRF Ensemble Prediction System in Taiwan

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Abstract The use of renewable energy is on the rise; however, it brings more challenges for grid operations and unit scheduling due to the intermittent nature and limitations in predicting renewable power generation. High-quality meteorological forecasts play a critical role in the effective application and management of renewable energy resources. This study evaluates the quality and economic benefits of probabilistic forecasts for 100-m wind speeds from the Weather Research and Forecasting model ensemble prediction system (WEPS). A multiple linear regression (MLR) method for calibration is also used to improve forecast quality. Additionally, this study explores the application of probabilistic hub-height wind speed forecasts in conjunction with an economic value analysis to determine whether wind turbines should be shut down during typhoon periods. The findings reveal that the forecast in the central offshore region of Taiwan exhibits the highest reliability and discrimination ability compared to those in the northern and southern regions. Additionally, employing the MLR calibration method significantly improved the reliability and discrimination ability of the probabilistic forecasts compared to the raw forecasts. Furthermore, during the typhoon seasons, almost all users, regardless of their cost-loss ratio, can benefit from basing their decisions on WEPS forecasts compared to using a single deterministic forecast. Importantly, decision-makers can benefit more from probabilistic than ensemble mean forecasts when both are derived from the same ensemble prediction system, since the former incorporates forecast uncertainty.