Forecasting in wind energy applications with site-adaptive Weibull estimation
Forecasting in wind energy applications with site-adaptive Weibull estimation
复制标题
DOI：
10.1109/icassp.2014.6853986
复制
发表时间：
2014-05
期刊：
影响因子：
--
通讯作者：
Matthew J. Holland;K. Ikeda
中科院分区：
文献类型：
--
作者：
Matthew J. Holland;K. Ikeda
从最佳供应决策到预期控制系统，风能应用在很大程度上依赖于对未来风速的准确、局部、短期预测。最近的研究表明，连续排名概率得分（CRPS）最小化模型与高斯假设是有效的，充分研究的网站，这些假设是适当的。我们考虑更一般的情况下，高斯性不假设和访问历史数据可能会受到限制。推导出一个CRPS表达式的最小极值分布，我们用它来提出一个网站自适应的威布尔为基础的CRPS最小化模型，这是测试，并表现出比确定性和概率性的参考模型在地面阵列的天气观测站在日本北方。
From optimal supply decisions to anticipatory control systems, wind-based energy applications rely heavily upon accurate, local, short-term forecasts of future wind speed. Recent studies have shown continuous ranked probability score (CRPS) minimizing models with Gaussian assumptions to be effective for well-researched sites where those assumptions are appropriate. We consider the more general case where Gaussianity is not assumed and access to historical data may be constrained. Deriving a CRPS expression for a minimum Extreme Value distribution, we use it to propose a site-adaptive Weibull-based CRPS-minimizing model, which is tested and shown to perform better than both deterministic and probabilistic reference models on a ground-based array of weather observation sites in northern Japan.