The development of wind energy in West Africa is essential to meet the rising energy needs due to population growth and societal development. However, only few studies have investigated the changes in turbine hub-height wind characteristics over the region under changing climate. This study aims at assessing the impact of climate change on wind power density (WPD) over West Africa using the simulations from the newly developed Coupled Model Intercomparison Project version 6 (CMIP6) models. The CMIP6 near-surface wind speed and directions simulations for the historical climate (1985–2014) were compared with ERA5 reanalysis data using multiple descriptive statistics. Relative to ERA5 reanalysis, the CMIP6 models alongside their multimodel ensemble mean (EnsMean) realistically reproduce the near-surface wind characteristics (i.e. wind speed and directions) across most subregions of West Africa, although noticeable biases still exist. Overall, the CMIP6 EnsMean performs better than most individual models at capturing the near-surface wind speed over the region. Under global warming, we find a robust projected increase (about 70%) in WPD over the Guinea coast subregion of West Africa, especially in June–July–August season. The December–January–February and March–April–May seasons show alternating projected WPD increase and decrease, with predominantly robust projected decrease over the Sahel subregion. The projected increase over the Guinea coast has strong temporal qualities, with the end of the century (2070–2099) changes showing stronger magnitude compared to the mid-century (2040–2069) changes, and thus may provide a commercially viable renewable energy source.

Estimation errors have constantly been a technology bother for wind power management, often time with deviations of actual power curve (APC) from the turbine power curve (TPC). Power output dispersion for an operational 800 kW turbine was analyzed using three averaging tine steps of 1-min, 5-min, and 15-min. The error between the APC and TPC in kWh was about 25% on average, irrespective of the time of the day, although higher magnitudes of error were observed during low wind speeds and poor wind conditions. The 15-min averaged time series proved more suitable for grid management and energy load scheduling, but the error margin was still a major concern. An effective power curve (EPC) based on the polynomial parametric wind turbine power curve modeling technique was calibrated using turbine and site-specific performance data. The EPC reduced estimation error to about 3% in the aforementioned time series during very good wind conditions. By integrating statistical wind speed forecasting methods and site-specific EPCs, wind power forecasting and management can be significantly improved without compromising grid stability.

The energy industry is faced with important investment and optimization choices especially for wind power as a fuel of the future, especially for China which boasts the largest installed wind power capacity. This study therefore assessed the potential status of future wind power over China using Coupled Model Intercomparison Project phase 5 (CMIP5) models. Changes in wind power density relative to the current time period 1981–2005 were then analyzed using near-surface wind speeds extrapolated to hub-height of 90 m above ground level. The results showed relatively modest differences between the models and reanalysis. The majority of the models showed any two of location, shape, and size agreement for peak areas albeit models BCC-CSM-1-1-M, BNU-ESM, and CanESM2 tended to overestimate wind speed by up to 2.5 m/s. The multi-model ensemble mean performed better than most individual models in representing the wind characteristics over the study area. Future changes in wind power density showed an increase (decrease) over the coastal areas of the South China Sea and Bay of Bengal (areas along the 30°–40° N belt). In all, the changes were not significant enough to neither warrant a move away from wind energy nor threaten considerably the marketability and profitability under the present warming scenario rate.