Multivariable Estimation of Wind Shear Profiles and Optimal Wind Turbine Energy Prediction

风能是可再生能源组合的主要贡献者之一。 在过去的15年里，这一全球性行业的装机容量经历了显著的持续增长，尽管2013年略有下降，但一些专家预测，到2020年装机容量将翻一番。涡轮机制造商之间的竞争已经造成了涡轮机成本的下降趋势，然而，对分布式发电和次优站点（具有较低风速、较高湍流和/或切变的站点）的兴趣增加，需要准确且精确地预测轮毂高度处的风速和涡轮机能量输出，这是需要对风切变进行真实估计的因素。 风切变的估计值可以根据风速计数据和垂直外推法，使用幂律或对数律模型产生，然后用于预测拟议轮毂高度的风速。然而，人们普遍认识到，这种方法具有高度的不确定性，影响到整个项目的不确定性。金融机构使用P90、P75或P50（分别在90%、75%和50%的时间内达到预测年能源产量的概率）来评估与潜在项目相关的风险，这是一个影响以下能力的因素：（a）获得资金，（B）建立债务比率和（c）杠杆利率。

Wind energy is one of the major contributors to the renewable energy mix. This global industry has experienced significant, sustained growth in installed capacity over the past 15 years and although it experienced a slight decline in 2013, some experts predict that installed capacity will double by 2020. Competition between turbine manufacturers has created a downward trend in turbine costs, however increasing interest in distributed generation and sub-optimal sites (those with lower wind speeds, higher turbulence and/or shear), demand an accurate and precise prediction of wind speed and turbine energy output at hub height, a factor that requires a true estimation of wind shear. An estimate of wind shear can be produced using power or logarithmic law models based on anemometer data and vertical extrapolation then used to predict wind speed at the proposed hub height. It is generally recognized however that such methods suffer from high levels of uncertainty which impacts the overall project uncertainty. Financial institutions use P90, P75 or P50 (the probability of reaching predicted annual energy output 90%, 75% and 50% of the time respectively) to evaluate the risk associated with a potential project, a factor that impacts the ability to (a) obtain funding, (b) establish debt ratio and (c) leverage interest rates.