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Multistage Wind-Electric Power Forecast by Using a Combination of Advanced Statistical Methods

机译：结合高级统计方法的多级风电预测

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摘要

A multistage advanced statistical method has been proposed for the real-time wind-electric power generation forecast of wind power plants (WPPs) based on a combination of artificial neural network (ANN) and support vector machine (SVM) models. In the first stage, output data of wind speed and wind direction from different numerical weather prediction (NWP) models are chosen among a set of grid points in the neighborhood of each WPP to train the ANN and SVM models. The best grids are then selected from those NWP grid data giving the minimum training error, and used for training and testing the developed wind-electric power forecast models. In the second stage, for each NWP data, ANN and SVM models are applied separately. The forecast errors are corrected by applying model output statistics (MOS) at the third stage. Different 48-h ahead forecasts of wind-electric power are then combined at the fourth stage by appropriate weighting factors to obtain an intermediate 48-h ahead forecast of the electrical power generated from wind. In the final stage, these forecast data are recombined to give an ultimate forecast. The proposed model is tested on 25 WPPs satisfactorily. The performance of the proposed multistage cascaded statistical model is compared with the available benchmark models and actual wind-electric power generation data. It has been shown that the proposed model performs better than the reference models in terms of short-term forecast accuracy, especially for WPPs in complex terrains with a scattered wind regime.
机译：基于人工神经网络（ANN）和支持向量机（SVM）模型的结合，提出了一种多阶段高级统计方法，用于风电厂（WPPs）的实时风电发电量预测。在第一阶段，从每个WPP附近的一组网格点中选择来自不同数值天气预报（NWP）模型的风速和风向的输出数据，以训练ANN和SVM模型。然后从给出最小训练误差的NWP网格数据中选择最佳网格，并将其用于训练和测试已开发的风电功率预测模型。在第二阶段，对于每个NWP数据，分别应用ANN和SVM模型。通过在第三阶段应用模型输出统计信息（MOS）纠正预测误差。然后在第四阶段通过适当的加权因子组合不同的48小时风电提前预报，以获得对风电产生的48小时提前中间预报。在最后阶段，将这些预测数据重新组合以给出最终预测。该模型在25个WPP上得到了令人满意的测试。将所提出的多级级联统计模型的性能与可用的基准模型和实际的风力发电数据进行比较。结果表明，就短期预报准确性而言，该模型的性能优于参考模型，特别是对于风沙较为分散的复杂地形中的WPP。

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