Wind speed prediction plays an essential role in wind energy utilization. However, most existing studies of wind speed forecasting used data from one location to build models and forecasts, which limited the accuracy of wind speed forecasting. Therefore, to improve the prediction accuracy at a target location, this study proposes a multiple-point model based on data from multiple locations for short-term wind speed prediction. The model, which utilizes wind speed measurements from neighboring locations and combines the extreme learning machine (ELM) with the AdaBoost algorithm, is named the multiple-point-AdaBoost-ELM model. Data from seventeen automatic meteorological stations in the Heihe River Basin are used, four stations at different positions are taken as target stations for multi-time-scale wind speed prediction, and six models and several metrics are involved for comparative analysis and comprehensive evaluation. The results show that: (1) the prediction performance of the proposed multiple-point-AdaBoost-ELM model is significantly superior to that of the compared single-point models; (2) the prediction accuracy of the multiple-point-AdaBoost-ELM model is relatively less affected by the prediction time-scale than that of the corresponding single-point model; and (3) the stations located at the center of multiple stations can obtain more accurate prediction results than those located near the edges of the region. Therefore, the proposed multiple-point-AdaBoost-ELM model is a more promising method than traditional single-point modeling methods. The proposed method fully uses historical wind speed at surrounding locations to enhance the wind speed predictions at target locations, makes up for the deficiency of the wind speed forecasting using data from one location, and expands a new way for wind speed prediction.

中文翻译：

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使用邻近位置的测量值并结合极限学习机和 AdaBoost 算法来预测风速

风速预测在风能利用中起着至关重要的作用。然而，现有的风速预测研究大多使用一个地点的数据来建立模型和预测，这限制了风速预测的准确性。因此，为了提高目标位置的预测精度，本研究提出了一种基于多个位置数据的多点模型来进行短期风速预测。该模型利用邻近位置的风速测量并将极限学习机 (ELM) 与 AdaBoost 算法相结合，被命名为多点 AdaBoost-ELM 模型。利用黑河流域17个自动气象站的数据，以不同位置的4个站作为多时间尺度风速预测的目标站，采用6个模型和多种指标进行对比分析和综合评价。结果表明：（1）所提出的多点AdaBoost-ELM模型的预测性能显着优于对比的单点模型； (2)多点AdaBoost-ELM模型的预测精度比相应的单点模型受预测时间尺度的影响相对较小; (3)位于多个站点中心的站点比位于区域边缘的站点可以获得更准确的预测结果。因此，所提出的多点AdaBoost-ELM模型是比传统单点建模方法更有前景的方法。该方法充分利用周边地点的历史风速来增强目标地点的风速预测，弥补了利用单点数据进行风速预测的不足，拓展了风速预测的新途径。