165951 20260116002212.0 doi 10.3390/app152413147 sideral 147437 ART-2025-147437 eng Lázaro, Roberto Wind Reference Year: A New Approach 2025 The representativeness of long-term wind data at a site remains a challenge, as it is essential for resource analysis, production adjustment in operating plants, and the simulation of hybridised plants. A representative one-year hourly time series, known as a Wind Reference Year (WRY), is required, yet the availability of long-term real data is rare, making the estimation of WRY from reanalysis data and shorter measurement campaigns a common approach. In this study, Gaussian Mixture Copula Models (GMCM) and five regression models were applied and compared. The GMCM was trained using 15 years of reanalysis data to generate simulations, and subsequently, regression-based Measure–Correlate–Predict (MCP) methods were applied to adapt the simulated reference year to site-specific conditions. Finally, the Hungarian algorithm was used to reorder the simulated data series, aligning it with a typical wind pattern and producing the WRY dataset. The results were validated against 15 years of real measurements and benchmarked against a heuristic method based on long-term similarity of main wind parameters and the commercial tool Windographer. The findings demonstrate the potential of the proposed method, showing improvements over existing techniques and providing a robust approach to constructing representative WRY datasets. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Melero, Julio J. Arregui, Sergio 15, 24 (2025), 13147 [21 pp.] Appl. sci. Applied Sciences (Switzerland) 2076-3417 1029978 http://zaguan.unizar.es/record/165951/files/texto_completo.pdf Versión publicada 2422710 http://zaguan.unizar.es/record/165951/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:165951 articulos driver 2026-01-15-21:57:15 ARTICLE