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Artificial neural network approach to spatial estimation of wind velocity data

机译：人工神经网络方法在风速数据空间估计中的应用

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摘要

In any regional wind energy assessment, equal wind velocity or energy lines provide a common basis for meaningful interpretations that furnish essential information for proper design purposes. In order to achieve regional variation descriptions, there are methods of optimum interpolation with classical weighting functions or variogram methods in Kriging methodology. Generally, the weighting functions are logically and geometrically deduced in a deterministic manner, and hence, they are imaginary first approximations for regional variability assessments, such as wind velocity. Geometrical weighting functions are necessary for regional estimation of the regional variable at a location with no measurement, which is referred to as the pivot station from the measurements of a set of surrounding stations. In this paper, weighting factors of surrounding stations necessary for the prediction of a pivot station are presented by an artificial neural network (ANN) technique. The wind speed prediction results are compared with measured values at a pivot station. Daily wind velocity measurements in the Marmara region from 1993 to 1997 are considered for application of the ANN methodology. The model is more appropriate for winter period daily wind velocities, which are significant for energy generation in the study area. Trigonometric point cumulative semivariogram (TPCSV) approach results are compared with the ANN estimations for the same set of data by considering the correlation coefficient (R). Under and over estimation problems in objective analysis can be avoided by the ANN approach.
机译：在任何区域风能评估中，相等的风速或能量线为有意义的解释提供了通用基础，这些解释为正确的设计目的提供了必要的信息。为了获得区域变化描述，在克里格方法中有使用经典加权函数或变异函数方法的最佳插值方法。通常，加权函数以确定性的方式在逻辑上和几何上推导，因此，它们是用于区域变异性评估（例如风速）的虚构第一近似。几何加权函数对于在没有测量值的位置进行区域变量的区域估计而言是必需的，该位置从一组周围测站的测量结果中称为枢轴测站。在本文中，通过人工神经网络（ANN）技术提出了枢纽站预测所需的周边站的加权因子。将风速预测结果与枢纽站的测量值进行比较。考虑将ANN方法应用于1993年至1997年马尔马拉地区的每日风速测量。该模型更适合于冬季的日常风速，这对于研究区域的能源产生至关重要。通过考虑相关系数（R），将三角点累加半变异函数（TPCSV）的方法结果与同一数据集的ANN估计进行比较。通过ANN方法可以避免客观分析中的过低和过高估计问题。

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