Offshore wind resource assessment by characterizing weather regimes based on self-organizing map

Abstract As offshore wind power is continuously integrated into the electric systems in around world, it critical to understand its variability. Weather regimes (WRs) can provide meteorological explanations for fluctuations power. Instead of relying on traditional large-scale circulation WRs, this study focuses assessing dependency resources WRs tailored region clustered based finer spatial scale. For purpose, we have applied self-organizing map algorithm cluster atmospheric circulations over South China Sea (SCS) and characterized classified WRs. Results show that at mesoscale effectively capture weather driving production variability, especially multi-day timescale. Capacity factor reconstruction during four seasons illustrates highly influence most areas winter southern part SCS summer, serve as a source predicting potential resources. In addition, further qualify intermittency complementarity under different which not been assessed associated with During changeable atmosphere conditions, high coastal reduce impact generation. The proposed approach able be implemented any may benefit resource evaluation characterization.