Distribution inference of wind speed at adjacent spaces using generative conditional distribution sampler

Abstract

Wind resource assessment is crucial for establishing wind farms and prediction of their economic benefits. The one key problem for wind resource assessment is to estimate the probability distribution of wind speed. In this study, we propose a nonparametric generative approach based generative conditional distribution sampler (GCDS) to sample wind speed data at different locations, which is equivalent to estimating wind speed distribution. The proposed approach can used to fit wind speed data and infer the distribution of wind speed at new locations with no observations. The proposed approach reduces the transmission and accumulation of errors caused by traditional interpolation methods. The analysis results show that the proposed method outperforms other models under key metrics, the improvement is generally over 14.7% for distribution fitting and interpolation fitting.