With the increasing penetration of wind power in renewable energy systems, it is important to improve the accuracy of wind speed prediction. However, wind power generation has great uncertainties which make high-quality interval prediction a challenge. Existing multi-objective optimization interval prediction methods do not consider the robustness of the model. Thus, trained models for wind speed interval prediction may not be optimal for future predictions. In this paper, the prediction interval coverage probability, the prediction interval average width, and the robustness of the model are used as three objective functions for determining the optimal model of short-term wind speed interval prediction using multi-objective optimization. Furthermore, a new Stochastic Sensitivity for Prediction Intervals (SS_PIs) is proposed in this work to measure the stability and robustness of the model for interval prediction. Using wind farm data from countries on two different continents as case studies, experimental results show that the proposed method yields better prediction intervals in terms of all metrics including prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW) and SS_PIs. For example, at the prediction interval nominal confidence (PINC) of 85%, 90% and 95%, the proposed method has the best performance in all metrics of the USA wind farm dataset.

随着风能在可再生能源系统中的渗透率不断提高，提高风速预测的准确性非常重要。然而，风力发电具有很大的不确定性，这使得高质量的间隔预测成为挑战。现有的多目标优化间隔预测方法没有考虑模型的鲁棒性。因此，用于风速间隔预测的训练模型对于未来的预测可能不是最佳的。在本文中，将预测间隔覆盖概率，预测间隔平均宽度和模型的鲁棒性作为三个目标函数，以确定使用多目标优化的短期风速间隔预测的最优模型。此外，在这项工作中，提出了一种新的预测区间随机敏感性（SS_PIs），以测量区间预测模型的稳定性和鲁棒性。使用来自两个不同大陆的国家的风电场数据进行案例研究，实验结果表明，该方法在所有指标（包括预测间隔覆盖率（PICP），预测间隔归一化平均宽度（PINAW）和SS_PIs）方面均产生了更好的预测间隔。例如，在85％，90％和95％的预测区间标称置信度（PINC）下，该方法在美国风电场数据集的所有指标中均具有最佳性能。实验结果表明，该方法在包括预测间隔覆盖率（PICP），预测间隔归一化平均宽度（PINAW）和SS_PIs在内的所有指标上均产生了更好的预测间隔。例如，在85％，90％和95％的预测区间标称置信度（PINC）下，该方法在美国风电场数据集的所有指标中均具有最佳性能。实验结果表明，该方法在包括预测间隔覆盖率（PICP），预测间隔归一化平均宽度（PINAW）和SS_PIs在内的所有指标上均产生了更好的预测间隔。例如，在85％，90％和95％的预测区间标称置信度（PINC）下，该方法在美国风电场数据集的所有指标中均具有最佳性能。