对时空缺失数据不敏感的基于图的大规模概率光伏功率预测

IF 8.6 1区 工程技术 Q1 ENERGY & FUELS IEEE Transactions on Sustainable Energy Pub Date : 2024-08-21 DOI:10.1109/TSTE.2024.3447023
Keunju Song;Minsoo Kim;Hongseok Kim
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引用次数: 0

摘要

近年来,分布式能源集成电力系统被认为是减缓气候变化的重要手段。然而,这使得电力系统更加不确定和复杂,因此可再生能源的大规模渗透需要考虑具有不确定性的准确预测。为此,我们提出了一个可扩展和缺失不敏感的框架,用于概率多站点光伏(PV)功率预测,特别关注大规模光伏站点和时空缺失数据。利用图神经网络(GNN),本文提出的具有随机粗图注意和概率时空学习的可扩展图学习机制在预测精度和模型训练复杂度方面对大规模PV站点具有较好的效果。同时,我们的框架分别在空间和时间域自适应地对缺失的PV数据进行补全。消融研究结果表明,我们的框架可以有效地提取大型PV站点的复杂时空特征。在大量的实验中,我们的框架对1600多个光伏站点和三种时空缺失数据的预测平均提高了7 ~ 10%和6 ~ 25%,确保了预测的准确性和稳定性。
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Graph-Based Large Scale Probabilistic PV Power Forecasting Insensitive to Space-Time Missing Data
In recent years, power systems integrated with distributed energy resources (DERs) have been considered to mitigate climate change. However, this makes power systems even more uncertain and complex, so uncertainty-aware accurate forecasting needs to be considered for the massive penetration of renewable energy. To this end, we propose a scalable and missing-insensitive framework for probabilistic multi-site photovoltaic (PV) power forecasting, specifically focused on large-scale PV sites and space-time missing data. By leveraging the graph neural network (GNN), the proposed scalable graph learning mechanism with random coarse graph attention and probabilistic spatio-temporal learning performs efficiently for large-scale PV sites in terms of forecasting accuracy and model training complexity. At the same time, our framework adaptively imputes the missing PV data in the space and time domain, respectively. Ablation study results demonstrate that our framework is effective for extracting complex spatial-temporal features across large-scale PV sites. Under extensive experiments, our framework shows 7 $-$ 10% and 6 $-$ 25% improvement on average for over 1600 PV sites and three types of space-time missing data, which ensures accurate and stable forecasting.
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来源期刊
IEEE Transactions on Sustainable Energy
IEEE Transactions on Sustainable Energy ENERGY & FUELS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
21.40
自引率
5.70%
发文量
215
审稿时长
5 months
期刊介绍: The IEEE Transactions on Sustainable Energy serves as a pivotal platform for sharing groundbreaking research findings on sustainable energy systems, with a focus on their seamless integration into power transmission and/or distribution grids. The journal showcases original research spanning the design, implementation, grid-integration, and control of sustainable energy technologies and systems. Additionally, the Transactions warmly welcomes manuscripts addressing the design, implementation, and evaluation of power systems influenced by sustainable energy systems and devices.
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