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基于多元周期性网格与云模型混合通道的功率预测研究(
)
《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]
- 卷:
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49
- 期数:
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2026年02期
- 页码:
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110-119
- 栏目:
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计算机科学与技术
- 出版日期:
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2026-04-10
文章信息/Info
- Title:
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A Study of Power Prediction Based on a Hybrid Channel of Multivariate Periodic Grids and Cloud Models
- 文章编号:
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1001-4616(2026)02-0110-10
- 作者:
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刘志仁1; 杜云龙2; 颜全椿3; 柴 赟2; 曹卫青3; 杨勤胜3
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1.国网江苏省电力有限公司无锡供电分公司,江苏 无锡 214000
2.国网江苏省电力有限公司,江苏 南京 210098
3.江苏方天电力技术有限公司,江苏 南京 211000
- Author(s):
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Liu Zhiren2; Du Yunlong1; Yan Quanchun3; Chai Yun1; Cao Weiqing3; Yang Qinsheng3
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1.State Grid Jiangsu Electric Power Co.,Ltd. Wuxi Power Supply Branch,Wuxi 214000,China
2.Jiangsu Electric Power Co.,Ltd.,Nanjing 210098,China
3.Jiangsu Frontier Power Technology Co.,Ltd.,Nanjing 211000,China
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- 关键词:
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- Keywords:
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- 分类号:
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O643/X703
- DOI:
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10.3969/j.issn.1001-4616.2026.02.011
- 文献标志码:
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A
- 摘要:
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随着新能源发电技术的广泛普及,功率预测已成为该领域不可或缺的研究方向. 然而,传统预测方法在面对不同时间尺度及多元因素影响时,其新能源功率预测模型的动态性能表现出明显局限性,致使预测精度难以达到理想水平. 针对该问题本研究提出一种结合多元周期性网格与云模型混合通道的功率预测技术研究,通过卡尔曼滤波器更新多元周期性网格,使其能实时适应复杂环境变化,精准捕捉时空动态特征,实现周期特征动态优化. 同时,利用云模型混合通道的隶属度函数与概率修正优化,融合多尺度信息并动态调整. 仿真结果表明,该方法有效提升了新能源功率预测在不同时空尺度下的准确性与稳定性,为新能源领域功率预测提供了更有效的解决方案. 在光伏功率预测中具有显著的推广意义,为电力系统调度优化、负荷管理和智能化控制提供有力支持,对促进光伏电站稳定运行和推动绿色能源发展具有重要的应用价值.
- Abstract:
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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.
更新日期/Last Update:
2026-04-10