Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing strategy, a novel optimization model, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming.

中文翻译：

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先进的风速点和间隔预报混合预报系统

超短期风速预测可以在短期内协助风力涡轮机的运行和调度，并进一步减少风电集成的不利影响。但是，由于风是不规则，非线性且不稳定的，因此准确预测风速是一项艰巨的任务。为此，研究人员进行了许多尝试。但是，他们通常仅使用点预测或间隔预测，从而导致预测结果不完善。因此，在本文中，我们开发了一种集成了高级数据预处理策略，新颖的优化模型和多种预测算法的预测系统。该组合预测系统可以克服传统预测方法固有的缺点，可以进一步提高预测性能。为了测试预测系统的有效性，来自西班牙Sotavento风电场的10分钟和1小时风速序列被用于进行比较实验。区间预测和点预测的结果都表明，在预测能力和稳定性方面，所提出的系统优于比较模型。因此，由于最小的预测误差和出色的泛化能力，我们认为此预测系统是辅助智能电网编程的有效工具。