论文标题
自适应通用logit正态分布短期预测
Adaptive Generalized Logit-Normal Distributions for Wind Power Short-Term Forecasting
论文作者
论文摘要
对非常短期和高分辨率的风能预测（从前方的几分钟到几个小时），尤其是海上的兴趣越来越大。统计方法是最重要的，因为天气预报对于这些交货时间不能提供信息。这些方法应该解释了这样一个事实，即风力发电作为随机过程是非平稳的，双键（由零和涡轮机的标称功率）和非线性的事实。适应这些方面可能会改善点和概率的预测。我们在这里提议专注于广义logit正态分布，这些分布自然适合且灵活地用于双键和非线性过程。相关参数是通过最大似然推理估算的。描述了估计方法的批处理和在线版本 - 在线版本允许通过分布参数的变化来额外处理非平稳性。在丹麦的Anholt海上风电场的测试案例上采用和分析了该方法，重点放在10分钟的点和概率预测上。
There is increasing interest in very short-term and higher-resolution wind power forecasting (from minutes to hours ahead), especially offshore. Statistical methods are of utmost relevance, since weather forecasts cannot be informative for those lead times. Those approaches ought to account for the fact that wind power generation as a stochastic process is nonstationary, double-bounded (by zero and the nominal power of the turbine) and non-linear. Accommodating those aspects may lead to improving both point and probabilistic forecasts. We propose here to focus on generalized logit-normal distributions, which are naturally suitable and flexible for double-bounded and non-linear processes. Relevant parameters are estimated via maximum likelihood inference. Both batch and online versions of the estimation approach are described -- the online version permitting to additionally handle non-stationarity through the variation of distribution parameters. The approach is applied and analysed on the test case of the Anholt offshore wind farm in Denmark, with emphasis placed on 10-min-ahead point and probabilistic forecasts.
