基于相似时段和PCA-ELM 的超短期风电功率预测

为提高风电场输出功率的预测精度， 提出一种采用相似时段选取原则和基于主成分分析(principal component analysis，PCA)与多层自编码极限学习机(multi-layer auto encoder extreme learning machine，ML-AE-ELM) 组合算法(PCA-ELM)的预测模型。通过关联度分析明确待测时段的相似时段范围，结合天气数据、机组状态和历史 功率构建训练和测试样本，利用预测算法完成样本的训练和测试，得到输出功率预测结果并验证。实验结果表明： 与常见的算法模型相比，该预测模型在不同装机容量和不同工作状态的风电场中均具有较高的预测精度，表现出良 好的预测稳定性和泛化能力。

Ultra-Short-Term Wind Power Forecasting Based on Similar Time Interval and PCA-ELM

In order to improve the forecasting accuracy of wind farm output power, a method based on the principle of similar time period selection and the principle of principal component analysis (PCA) is proposed. The forecasting model is based on the combination of PCA and multi-layer auto encoder extreme learning machine (ML-AE-ELM). Through the correlation analysis, the range of similar periods of time to be tested is determined, and the training and test samples are constructed by combining weather data, unit status and historical power, and the forecasting algorithm is used to complete the training and test of the samples, so as to obtain the forecasting results of output power and verify them. The experimental results show that compared with the common algorithm model, the proposed model has higher forecasting accuracy in different installed capacity and different working conditions of wind farms, and shows good forecasting stability and generalization ability.