000165951 001__ 165951 000165951 005__ 20260116002212.0 000165951 0247_ $$2doi$$a10.3390/app152413147 000165951 0248_ $$2sideral$$a147437 000165951 037__ $$aART-2025-147437 000165951 041__ $$aeng 000165951 100__ $$aLázaro, Roberto 000165951 245__ $$aWind Reference Year: A New Approach 000165951 260__ $$c2025 000165951 5060_ $$aAccess copy available to the general public$$fUnrestricted 000165951 5203_ $$aThe 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. 000165951 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000165951 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000165951 700__ $$aMelero, Julio J. 000165951 700__ $$aArregui, Sergio 000165951 773__ $$g15, 24 (2025), 13147 [21 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417 000165951 8564_ $$s1029978$$uhttps://zaguan.unizar.es/record/165951/files/texto_completo.pdf$$yVersión publicada 000165951 8564_ $$s2422710$$uhttps://zaguan.unizar.es/record/165951/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000165951 909CO $$ooai:zaguan.unizar.es:165951$$particulos$$pdriver 000165951 951__ $$a2026-01-15-21:57:15 000165951 980__ $$aARTICLE