基于SAIGM-KELM的短期风电功率预测

针对时序下风电功率的随机性和波动性问题，提出一种基于自适应智能灰色系统(SAIGM)和遗传算法优化核极限学习机(GA-KELM)的混合风电功率预测模型。首先，以灰色关联性分析不同季度下风向量与数值气象预报(NWP)对风电功率的影响为基础，采用自适应智能灰色系统预测风速，并将预测的风速与相连时序下的风向和NWP有效整合作为预测样本。其次，利用遗传算法优化核极限学习机搭建风电功率预测模型，并将实际风向量与NWP有效整合作为预测模型的训练样本。最后，利用优化后的预测模型实现不同季节的风电功率预测。实验表明混合预测模型可实现对风电功率的短期预测，预测结果具有准确性和可靠性。

Short-term wind power forecasting based on SAIGM-KELM

Given the problem caused by the randomness and volatility of wind power under time series, a hybrid wind power forecasting model based on a Self-Adaptive Intelligence Grey Predictive Model with Alterable Structure (SAIGM) and Genetic Algorithm Optimized Kernel Extreme Learning Machine (GA-KELM) is proposed. First, the influence of wind vector and Numerical Weather Prediction (NWP) on wind power in different seasons is analyzed by grey correlation, and the wind speed is predicted by an adaptive intelligent grey system. The predicted wind speed is effectively integrated with the actual wind vector and NWP in the adjacent time series as prediction samples. Secondly, the optimized kernel extreme learning machine based on a genetic algorithm is used to build the wind power prediction model, and the actual wind vector with NWP are also effectively integrated as training samples of the forecasting model. Finally, the optimized prediction model is used to achieve wind power forecasting in different seasons. Experiments demonstrate that the hybrid forecasting model can realize short-term wind power forecasting and the results are accurate and reliable. This work is supported by National Natural Science Foundation of China (No. 61973138).