基于变分模态和改进多元宇宙优化的短期风电功率预测
摘要：为提高风电功率预测精度，提出一种基于变分模态分解(variational mode decomposition, VMD)和改进多元宇宙算法(improved multiverse optimization, IMVO)优化极限学习机(extreme learning machine, ELM)的组合预测方法。首先借助VMD算法将原始风电数据分解为模态分量，并根据互信息熵划分为高、低频分量以简化数据。然后在传统多元宇宙算法基础上通过引入Tent混沌映射、指数型旅行距离率以及精英反向学习机制进行改进，并与ELM相结合得到IMVO-ELM预测模型。最后将高频、低频分量预测结果叠加，得到最终预测结果。仿真结果表明，IMVO-ELM模型预测精度、收敛速度对比ELM、MVO-ELM、PSO-ELM方法具有一定的优越性。且在借助VMD算法的数据预处理下，预测精度得到进一步提高，验证了所提组合预测方法的有效性。
Abstract:In order to improve the wind power forecasting accuracy of wind farms, a combined forecasting method based on variational mode decomposition (VMD) and improved multiverse algorithm (IMVO) optimized extreme learning machine (ELM) was proposed. Firstly, the original load data was decomposed into modal components with the help of VMD algorithm, and divided into high-frequency and low-frequency sequences according to mutual information entropy to simplify the data. Then, based on the traditional multiverse algorithm, it was improved by introducing Tent chaotic map, exponential travel distance rate and elite reverse learning mechanism, and combined with ELM to obtain the IMV0-ELM prediction model. Finally, the prediction results of the high frequency and low frequency components are superimposed to obtain the final prediction result. The experimental results show that the prediction accuracy and convergence speed of the IMVO-ELM model have certain advantages compared with the methods of ELM, MVO-ELM and PSO-ELM. And under the data preprocessing with the help of VMD algorithm, the prediction accuracy is further improved, which verifies the effectiveness of the proposed combined prediction method.
标题：基于变分模态和改进多元宇宙优化的短期风电功率预测
英文标题：Short-term Wind Power Forecasting Based on Variational Modes and Improved Multiverse Optimization
作者：何鑫, 雷勇, 王进武, 李云凤, 王小昔,
关键词：风电功率预测, 变分模态分解, 极限学习机, 改进多元宇宙算法, 混沌映射, 精英反向学习,
发表日期：2023-12-04
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