Wind energy resources will be impacted by climate change. A novel hybrid ensemble technique is presented to improve long-term wind speed projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) data from global climate models. The technique constructs an optimized system, which relies on a Genetic Algorithm and an Enhanced Colliding Bodies Optimization technique. Next, the performance of the proposed method over a target area (United Kingdom) is evaluated between 1950 and 2014. Finally, to avoid single-valued deterministic projections and mitigate the uncertainties, the improved wind speed data series are investigated considering different climate-change scenarios - the Shared Socioeconomic Pathways (SSPs) - for the period 2015-2050. The performance of different CMIP6 models is found to differ over time and space. In the target area the data derived from the Hybrid model confirm that extreme wind events will occur more frequently. The monthly mean wind speed is expected to increase from 3.41 m/s during 1950-2014 to 3.60, 3.63, 3.48, 3.59 and 3.61 m/s during the study period in the SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5 climate-change scenarios, respectively. More generally, the results prove that the Hybrid model is highly effective in improving the accuracy, direction and geographical patterns of the data, and this novel method can narrow the potential uncertainties of numerical simulations.
Keywords: Climate change; Weighted average ensemble; Wind direction projections; Wind speed projections.
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