Abstract At present, environmental pollution, climate warming and other problems are becoming more and more serious. And wind energy is pollution-free and never be exhausted, so it can make a major contribution to the global energy transformation. However, its random fluctuations and uncertainties bring adverse effects to the power system and endanger the safety of the power grid. Therefore, this paper combines artificial intelligence methods with statistical knowledge, and proposes a new interval prediction model based on the Fast Correlation Based Filter (FCBF) algorithm, the optimized Radial Basis Function (RBF) model and Fourier distribution for wind speed. Firstly considering environmental factors, this paper studies multi-factor wind speed prediction and applies the FCBF algorithm to filter the factors that affect the wind change. After that, this paper applies the idea of the Extremal Optimization (EO) to improve the Particle Swarm Optimization (PSO) and constructs a new EPSO optimization model for optimizing the RBF model. Next, using the Fourier function to fit the error probability distribution, and the wind speed interval is estimated based on point prediction results. Finally, the actual data of Changma Wind Farm is used for experiments to verify the feasibility and effectiveness of the proposed model. And through experimental results and comparison, it can be concluded: (1) Using the FCBF algorithm to select input variables can reduce redundant variables and lay a good foundation for subsequent prediction; (2) Applying the constructed EPSO-RBF model to predict wind speed, and the maximum and average value of the prediction error are only 0.8430 m/s, 0.1749 m/s, which is significantly better than several other traditional neural network models; (3) Introducing the Fourier function into the wind speed interval prediction, even at the 80% confidence level, the average width of the interval prediction is less than 3 m/s, and the coverage rate is higher than 90%.

中文翻译：

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基于人工智能方法和误差概率分布的短期风速区间预测

摘要 当前，环境污染、气候变暖等问题日益严重。而风能无污染、永不枯​​竭，可以为全球能源转型做出重大贡献。但其随机波动和不确定性给电力系统带来不利影响，危及电网安全。因此，本文将人工智能方法与统计知识相结合，提出了一种基于快速相关滤波器（FCBF）算法、优化径向基函数（RBF）模型和风速傅里叶分布的区间预测模型。本文首先考虑环境因素，研究多因素风速预测，应用FCBF算法过滤影响风速变化的因素。之后，本文应用极值优化（EO）的思想改进粒子群优化（PSO），构建新的EPSO优化模型来优化RBF模型。接下来，利用傅立叶函数拟合误差概率分布，根据点预测结果估计风速区间。最后，利用常马风电场的实际数据进行实验，验证所提模型的可行性和有效性。并通过实验结果和对比得出：（1）使用FCBF算法选择输入变量可以减少冗余变量，为后续预测打下良好的基础；(2) 应用构建的EPSO-RBF模型预测风速，预测误差的最大值和平均值仅为0.8430 m/s、0.1749 m/s，明显优于其他几种传统的神经网络模型；(3)在风速区间预测中引入傅里叶函数，即使在80%的置信水平下，区间预测的平均宽度小于3 m/s，覆盖率高于90%。