Probabilistic power curve estimation based on meteorological factors and density LSTM

Wind power curve describes the relationship between wind speed and wind power output, which is useful for wind farm design and wind turbine condition monitoring. However, most research on power curves modeling neglected the effect of historical meteorological factors. Also, the research on probabilistic power curve is still limited. Therefore, this paper proposes a novel probabilistic power curve modeling approach. The effect of both current and historical meteorological variables, such as wind speed, wind direction, ambient temperature and turbulence intensity, on the power curve is studied by binning method and kernel density estimation. Next, instead of forecasting the wind power or its quantiles, a new probabilistic power curve model, Density LSTM with Negative Log-Likelihood Loss, is proposed to forecast the parameters of the probability density function of wind power directly. Finally, different probability distribution of wind power output is studied. Gamma, Laplace and Weibull distributions prove to be more suitable than Gaussian distribution. Based on the datasets of inland and offshore wind farms, it is verified that adding useful historical meteorological variables can improve the forecasting performance of the test dataset. Besides, the proposed probabilistic power curve can effectively improve the prediction performance in the probabilistic prediction task and can improve the prediction precision of the annual energy production.