南京师大学报（自然科学版）2026，Vol.49Issue(2)：110-119,10.DOI:10.3969/j.issn.1001-4616.2026.02.011
基于多元周期性网格与云模型混合通道的功率预测研究
A Study of Power Prediction Based on a Hybrid Channel of Multivariate Periodic Grids and Cloud Models
摘要
Abstract
In the wake of the extensive dissemination and application of new energy generation technologies,power prediction has emerged as an essential research area within this domain.Nevertheless,conventional prediction methodologies exhibit conspicuous limitations in the dynamic capabilities of new energy power prediction models when confronted with diverse time scales and the impacts of multiple factors,thereby rendering the attainment of desired prediction accuracy a challenging feat.In response to this predicament,the present study proposes an innovative power prediction technique that amalgamates the multivariate periodic grid with the cloud model hybrid channel.Through the utilization of the Kalman filter,the multivariate periodic grid is continuously updated in real-time,enabling it to adeptly adapt to complex and fluctuating environmental conditions,precisely capture spatio-temporal dynamic characteristics,and effectuate the dynamic optimization of periodic features.Concurrently,the cloud model hybrid channel is enhanced by leveraging the membership function with probability correction to seamlessly integrate multi-scale information and effectuate dynamic adjustments.Simulation outcomes convincingly demonstrate that this method significantly augments the accuracy and stability of new energy power prediction across varying spatio-temporal scales,thereby proffering a more efficacious solution for power prediction within the new energy realm.This approach significantly enhances photovoltaic power forecasting,supporting power system dispatch optimization,load management,and intelligent control,while promoting the stable operation of photovoltaic plants and advancing green energy development.关键词
新能源功率预测/多元周期性网格/卡尔曼滤波器/云模型混合通道Key words
new energy power prediction/multivariate periodic grids/Kalman filter/cloud model hybrid channel分类
化学化工引用本文复制引用
刘志仁,杜云龙,颜全椿,柴赟,曹卫青,杨勤胜..基于多元周期性网格与云模型混合通道的功率预测研究[J].南京师大学报（自然科学版）,2026,49(2):110-119,10.基金项目
国网江苏省电力有限公司科技项目资助项目(J2023116). （J2023116）
