基于MMD-OHCP数据预处理的光伏出力超短期预测方法  

An Ultra-Short-Term Prediction Method of Photovoltaic Output Based on MMD-OHCP Data Preprocessing

作　　者：任宇路 程昱舒 王书姝 闫春蕊 郭晓霞 白志霞 REN Yulu;CHENG Yushu;WANG Shushu;YAN Chunrui;GUO Xiaoxia;BAI Zhixia(Marketing Service Center of State Grid Shanxi Power Company,Taiyuan 030032,Shanxi,China)

机构地区：[1]国网山西省电力公司营销服务中心,山西太原030032

出　　处：《电网与清洁能源》2025年第2期93-99,共7页Power System and Clean Energy

基　　金：国网山西省电力公司科技项目(52051L230004)。

摘　　要：随着光伏发电的大规模并网,电网安全问题日益凸显,提高光伏发电超短期预测的准确性可以有效预防此类问题的发生。提出一种基于数学形态学去噪(mathematicalmorphology denoising,MMD)和二分之一补点修正法(onehalf complementary point correction method,OHCP)相结合的数据预处理方法,通过对数据的清洗和平滑操作,使数据能够被机器算法高效识别和利用;利用皮尔逊相关系数分析光伏出力与气象因素之间的相关性,得出3个与光伏出力相关性较大的气象因素;建立BP(back propagation)、XG-Boost和LSTM(long short-term memory)3种预测模型对光伏出力进行超短期预测。仿真结果表明:所提模型在有限次迭代下,考虑3种主要影响因素时,预测的准确度最高。验证了所提模型及数据处理方法的有效性和可行性。With the large-scale grid-connection of PV power generation,the grid security problem is becoming more and more prominent,and improving the accuracy of the ultra-short-term prediction of PV power generation can effectively prevent the occurrenceof such problems.First,a data preprocessing method based on the combination of mathematical morphology denoising(MMD)and one-half complementary point correction(OHCP)is proposed to enable the data to be efficiently identified and utilized by machine algorithms through data cleaning and smoothing operations;subsequently,the correlation between PV output and meteorological factors is analyzed by using the Pearson correlation coefficient to derive the three meteorological factors that have the highest correlation with PV output;Finally,three prediction models:BP,XG-Boost and LSTM,are established to forecast the PV output in the ultra-short term.The simulation results show that the prediction models have the highest prediction accuracy when the three main influencing factors are considered under a finite number of iterations.The validity and feasibility of the proposed models and data processing methods are verified.

关 键 词：数学形态学去噪 二分之一补点修正法 气象因素 光伏出力 超短期预测 

分 类 号：TM615[电气工程—电力系统及自动化]