Novel strategies of Ensemble Model Output Statistics (EMOS) for calibrating wind speed/power forecasts. (1st November 2022)

• Record Type: Journal Article
• Authors: Casciaro, Gabriele; Ferrari, Francesco; Cavaiola, Mattia; Mazzino, Andrea
• Is Part Of: Energy conversion and management. Volume 271(2022)
• Publication Date: 2022-11-01
• Subjects: Long-term wind power forecasts -- Long-term wind speed forecasts -- Numerical weather prediction models -- Ensemble model output statistics -- Wind speed from SYNOP stations; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.116297
• Languages: English
• ISSNs: 0196-8904

Abstract

Abstract: The issue of the accuracy of wind speed/power forecasts is becoming more and more important as wind power production continues to increase year after year. Having accurate forecasts for the energy market clashes with intrinsic difficulties of wind forecasts due to, e.g., the coarse resolution of Numerical Weather Prediction models. Here, we propose a novel Ensemble Model Output Statistics (EMOS) which accounts for nonlinear relationships between predictands and both predictors and other weather observables used as conditioning variables. The strategy is computationally cheap and easy-to-implement with respect to other more complex strategies dealing with nonlinear regressions. Our novel strategy is assessed in a systematic way to quantify its added value with respect to ordinary, linear, EMOS strategies. Wind speed/power forecasts over Italy from the Ensemble Prediction System (EPS) in use at the European Centre for Medium-Range Weather Forecasts (ECMWF) are considered for this purpose. The calibrations are based on the use of past wind speed measurements collected by 69 SYNOP stations over Italy in the years 2018 and 2019. Our results show the key role played by conditioning variables to disentangle the model error (wind/power) thus allowing a net improvement of the calibration with respect to ordinary EMOS strategies. Graphical abstract: Highlights: From nonhomogeneous linear regressions to nonhomogeneous nonlinear regressions. Conditioning meteorological variables economically account for nonlinear feature. Nonlinear features greatly improve ordinary EMOS performances. Our wind speed calibration also improves the wind power forecast.