基于混合粒子群算法和多分位鲁棒极限学习机的短期风速预测方法

为实现高精度的短期风速预测,提出一种基于混合粒子群算法和多分位鲁棒极限学习机的短期风速预测方法。在信号处理阶段,利用时变滤波经验模态分解技术将原始风速序列分解为若干子模式以降低其不稳定性。然后采用混合粒子群算法对每一个子模式进行特征提取,接着利用多分位鲁棒极限学习机分别建立预测模型并利用混合粒子群算法进行参数优化,最后对每个子模式的预测值进行聚合计算得到最终的预测结果。仿真结果表明:在考虑使用混合粒子群算法进行特征提取和模型参数优化后,所提方法具有更高的预测精度。同时基于时变滤波法的经验模态分解技术能够进一步提高预测准确性。

Hybrid population particle algorithm and multi-quantile robust extreme learning machine based short-term wind speed forecasting

In order to achieve short-term wind speed prediction with high accuracy, this paper proposes a Hybrid Population Particle Algorithm (HPPA) and multi-Quantile Robust Extreme Learning Machine (QR-ORELM) based short-term wind speed forecasting. In signal processing stage, it adopts a Time Adaptive Filter based Empirical Mode Decomposition (TVF-EMD) to decompose the original wind speed series into several Intrinsic Mode Functions (IMFs) to decrease the volatility. Then HPPA is used to extract the features of each IMF, and QR-ORELM is used to build their forecasting models respectively and the parameters are optimized by HPPA. Finally, the eventual result can be obtained through aggregating the prediction value of each IMF. Simulation results show that after adopting HPPA for feature selection and model parameter optimization, the proposed hybrid method has higher prediction accuracy. Meanwhile, TVF-EMD based Wind Speed Forecasting (WSF) methods could further improve the predicting accuracy. This work is supported by National Natural Science Foundation of China (No. 51707041) and Science and Technology Project of China Southern Power Grid (No. GDKJXM20162087).