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A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction

机译：基于多倍气象输入数据的风电功率预测kNN分类器的新实现

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With the growing share of wind power production in the electric power grids, many critical challenges to the grid operators have been emerged in terms of the power balance, power quality, voltage support, frequency stability, load scheduling, unit commitment and spinning reserve calculations. To overcome such problems, numerous studies have been conducted to predict the wind power production, but a small number of them have attempted to improve the prediction accuracy by employing the multidimensional meteorological input data. The novelties of this study lie in the proposal of an efficient and easy to implement very short-term wind power prediction model based on the k-nearest neighbor classifier (kNN), in the usage of wind speed, wind direction, barometric pressure and air temperature parameters as the multi-tupled meteorological inputs and in the comparison of wind power prediction results with respect to the persistence reference model. As a result of the achieved patterns, we characterize the variation of wind power prediction errors according to the input tuples, distance measures and neighbor numbers, and uncover the most influential and the most ineffective meteorological parameters on the optimization of wind power prediction results. (C) 2017 Elsevier Ltd. All rights reserved.
机译：随着风力发电在电网中的份额不断增加，在功率平衡，电能质量，电压支持，频率稳定性，负载调度，机组承诺和自旋储备计算方面，对电网运营商提出了许多关键挑战。为了克服这些问题，已经进行了许多研究来预测风力发电，但是少数研究试图通过利用多维气象输入数据来提高预测精度。这项研究的新颖之处在于基于风速，风向，大气压力和空气的，基于k最近邻分类器（kNN）的高效且易于实现的非常短期风能预测模型的建议。温度参数作为多因子气象输入以及相对于持久性参考模型的风电预测结果的比较。作为已实现模式的结果，我们根据输入元组，距离度量和邻居数来表征风电预测误差的变化，并揭示对风电预测结果最优化的最有影响力和最无效的气象参数。 （C）2017 Elsevier Ltd.保留所有权利。

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