The intermittent nature of wind energy is a major challenge transforming the energy sector from fossil fuels to renewables. Depending on the location, results from previous studies show that the availability of wind energy can strongly vary over a year. However, although global temporal wind speed fluctuations are complex on the monthly and seasonal scales, they have been rarely quantified so far. Thus, the goals of this study were to assess the annual cycle and intra-annual variability of wind power around the world. A comprehensive dataset of more than 7000 globally distributed near-surface wind speed time series was analyzed. After extrapolation to a typical wind turbine hub height of 120 m, the monthly and seasonal mean wind speed, wind power density, and intra-annual variability were calculated. The system of wind speed distributions, which consists of the Burr-Generalized Extreme Value, Kappa, and Wakeby distributions, was fitted to all wind speed time series and used to estimate wind turbine related capacity factors. The greatest global wind resource was found for spring (global mean capacity factor: 0.272). In summer, the global wind resource decreased by 20.7%. The results reveal the greatest intra-annual variability in regions affected by the Indian monsoon circulation.

风能的间歇性是将能源部门从化石燃料转变为可再生能源的主要挑战。根据位置的不同，以前的研究结果表明，一年中风能的可用性可能会发生很大变化。但是，尽管全球时间风速波动在月度和季节尺度上很复杂，但迄今为止很少被量化。因此，本研究的目标是评估全球风力发电的年周期和年内变化。分析了7000多个全球分布的近地表风速时间序列的综合数据集。在外推至典型的120 m风力涡轮机轮毂高度后，计算出月度和季节平均风速，风能密度和年内变化率。风速分布系统 它由Burr-Generalized Extreme Value，Kappa和Wakeby分布组成，适用于所有风速时间序列，并用于估算与风力发电机相关的容量因子。发现春季最大的全球风能资源（全球平均风能系数：0.272）。夏季，全球风能资源减少了20.7％。结果表明，受印度季风环流影响的地区的年内变化最大。