摘要
近年来,国家大力推动分布式光伏发电,准确可靠的光伏功率预测对于保证大规模分布式光伏接入电网是必不可少的。当前的分布式光伏功率预测方法尚未充分考虑到气象因素的影响,难以提升预测精度。针对上述问题,提出一种基于天气变化过程的自适应分型与匹配的分布式光伏短期功率预测方法。首先通过K-Medoids-Grey实现天气过程的场景划分,再通过改进的多元宇宙算法对卷积神经网络进行优化,实现分布式光伏的短期预测。以中国甘肃省某分布式光伏用户为例进行验证。结果表明,在测试集中,IMVO-CNN方法在聚类情况下的预测精度比不聚类情况下预测精度提高9.83百分点,验证了该方法的有效性。
In recent years,the country has vigorously promoted distributed photovoltaic power generation,and accurate and reliable photovoltaic power prediction is essential to ensure large-scale distributed photovoltaic integration into the power grid.The current distributed photovoltaic power prediction methods have not fully considered the impact of meteorological factors,making it difficult to improve prediction accuracy.To address the above issues,a distributed photovoltaic short-term power prediction method based on adaptive classification and matching of weather change processes is proposed.First,scenario partitioning of weather processes is achieved through K-Medoids-Grey,and then the convolutional neural network is optimized using an improved multiverse algorithm to achieve short-term prediction of distributed photovoltaics.Taking a distributed photovoltaic user in Gansu province,China as an example for verification.The results show that in the test set,the prediction accuracy of the IMVO-CNN method under clustering is 9.83 percentage points higher than that under non clustering,verifying the effectiveness of the method.
作者
葛俊雄
蔡国伟
姜柳
庞振江
于同伟
赵武博文
Ge Junxiong;Cai Guowei;Jiang Liu;Pang Zhenjiang;Yu Tongwei;Zhao Wubowen(School of Electric Engineering,Northeast Electric Power University,Jilin 132012,Jilin,China;School of Information,Shenyang Institute of Engineering,Shenyang 110136,Liaoning,China;Shenzhen Guodian Technology Communication Co.,Ltd.,Shenzhen 518000,China;Electric Power Research Institute of State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110055,Liaoning,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2024年第15期288-299,共12页
Laser & Optoelectronics Progress
