针对光伏发电功率预测精度不高的问题,提出一种基于优化分解降噪联合误差修正模型。该模型分为3个阶段,第一阶段,首先用基于全局搜索的鲸鱼优化算法（GSWOA）选取变分模态分解（VMD）的参数,之后运用优化后的VMD对原始数据进行分解;然后利用互相关分析重构高频分量,最后对高频分量进行小波软阈值降噪（WTSD）;第二阶段,运用门控循环单元（GRU）对每个分量进行预测,将所有分量预测结果叠加起来得到初步预测结果;第三阶段,对初始预测结果进行误差修正（EC）。为验证模型的有效性,利用宁夏太阳山光伏电站2021年1、4、7、10月份的光伏实测数据进行实验,实验结果表明,相比于LSTM、GRU、VMD-LSTM,该混合模型表现出更好的性能。
Abstract
Aiming at the problem of low accuracy of photovoltaic power generation, the paper proposes a model based on optimized decomposition, noise reduction and error correction. The model is divided into three stages.In the first stage, the global search-based whale optimization algorithm（GSWOA） is used to select the parameters of the variational modal decomposition（VMD）,and then the optimized VMD is used to decompose the original data; then the high-frequency components are reconstructed using mutual correlation analysis, and finally wavelet soft-threshold noise reduction（WTSD） is applied to the high-frequency components; in the second stage, a gated recurrent unit（GRU） is used to predict each component, and the initial prediction results are obtained by superposing all the component prediction results; in the third stage, the initial prediction results are subjected to error correction（EC）. In order to verify the effectiveness of the model, the paper utilizes the measured PV data from Ningxia Sun Mountain PV power station in January, April, July and October 2021 to conduct experiments, and the experimental results show that the hybrid model exhibits better performance than LSTM, GRU, and VMD-LSTM.
关键词
光伏发电 /
功率预测 /
变分模态分解 /
门控循环单元 /
基于全局搜索的鲸鱼优化算法 /
小波软阈值 /
误差修正
Key words
photovoltaic power generation /
power prediction /
variational modal decomposition /
gated recurrent unit /
whale optimization algorithm based on global search /
wavelet soft thresholding /
error correction
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