由于光伏发电的随机性和不稳定性会影响功率预测的精度,提出一种基于皮尔逊相关系数（PCC）、K-均值算法（K-means）、变分模态分解（VMD）、麻雀搜索算法（SSA）、核函数极限学习机（ KELM）的光伏功率短期预测模型。首先,用PCC选取主要因素作为输入;K-均值算法进行相似日聚类,将历史数据聚类为晴天、多云和雨天;其次,VMD对原始信号进行分解,充分提取集合中的输入因素信息,提高数据质量;SSA优化KELM模型的核函数参数和正则化系数解决其参数选择敏感问题;最后,将不同序列预测值叠加得到最终预测结果。仿真结果表明,所提相似日聚类下PCC-VMD-SSA-KELM模型具有较小的预测误差。
Abstract
Because the randomness and instability of photovoltaic power generation will affect the accuracy of power prediction, this paper proposes a short-term photovoltaic power prediction model based on Pearson correlation coefficient （PCC）, K-means algorithm （K-means）, variational mode decomposition （VMD）, sparrow search algorithm （SSA）, and kernel based extreme learning machine （KELM）. Firstly, PCC is used to select the main factors as input; K-means algorithm clusters the historical data into sunny, cloudy and rainy days. Secondly, VMD decomposes the original signal to fully extract the input factor information in the set to improve the data quality. SSA optimizes the kernel function parameters and regularization coefficients of KELM model to solve its sensitive problem of parameter selection. Finally, the final prediction result is obtained by superimposing the prediction values of different series. The simulation results show that the PCC-VMD-SSA-KELM model with similar day clustering has small prediction error.
关键词
光伏发电 /
功率预测 /
变分模态分解 /
K-均值 /
麻雀算法 /
核函数极限学习机
Key words
photovoltaic power generation /
power forecasting /
variational mode decomposition /
K-means /
sparrow search algorithm /
kernel based extreme learning machine
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基金
国家自然科学基金（51877070）; 河北省重点研发计划（19214501D; 20314501D）; 河北省自然科学基金（E2021208008）
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