Hsu W.-KCHUNG-KEE YEH2022-04-252022-04-25202119961073https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109016160&doi=10.3390%2fen14123702&partnerID=40&md5=58311eb29959d616a1ec6d04f499d4c2https://scholars.lib.ntu.edu.tw/handle/123456789/605923In this study, we present the wind distributions from a long-term offshore met mast and a novel approach based on the measure–correlate–predict (MCP) method from short-term onshore-wind-turbine data. The annual energy production (AEP) and capacity factors (CFs) of one onshore and four offshore wind-turbine generators (WTG) available on the market are evaluated on the basis of wind-distribution analysis from both the real met mast and the MCP method. Here, we also consider the power loss from a 4-month light detection and ranging (LiDAR) power-curve test on an onshore turbine to enhance the accuracy of further AEP and CF evaluations. The achieved Weibull distributions could efficiently represent the probability distribution of wind-speed variation, mean wind speed (MWS), and both the scale and shape parameters of Weibull distribution in Taiwan sites. The power-loss effect is also considered when calculating the AEPs and CFs of different WTGs. Successful offshore wind development requires (1) quick, accurate, and economical harnessing of a wind resource and (2) selection of the most suitable and efficient turbine for a specific offshore site. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.Annual energy production (AEP)Capacity factors (CFs)Light detection and ranging (LiDAR)Mean wind speed (MWS)Measure–correlate–predict (MCP)Wind-turbine generator (WTG)Offshore oil well productionOptical radarWeibull distributionWindWind powerAnnual energy productionsCapacity factorsLight detection and rangingMean wind speedOnshore wind turbineScale and shape parametersWind distributionWind speed variationsOffshore wind turbinesjournal article10.3390/en141237022-s2.0-85109016160