Short-term power forecasting of distributed photovoltaic cluster based on fuzzy C-means clustering and GWO-GAT-BiGRU model

ZHANG Duxi; ZHAO Wenyan; CHEN Chao; ZHOU Fei; WU Qiuwei; SONG Guocheng

doi:10.11930/j.issn.1004-9649.202510068

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Short-term power forecasting of distributed photovoltaic cluster based on fuzzy C-means clustering and GWO-GAT-BiGRU model

更新时间：2026-10-14

- Short-term power forecasting of distributed photovoltaic cluster based on fuzzy C-means clustering and GWO-GAT-BiGRU model
- Vol. 59, Issue 9, Pages: 223-234(2026)
- 作者机构：
  
  1. 国网浙江省电力有限公司宁波供电公司
  2. 天津大学电气自动化与信息工程学院,天津,300072
- 作者简介：
- 基金信息：
  
  This work is supported by Joint Funds of the National Natural Science Foundation of China (No.U24B6008)
- DOI：10.11930/j.issn.1004-9649.202510068
  CLC：
- Published：2026
- 稿件说明：
移动端阅览
章杜锡, 赵文巘, 陈超, 等. 基于模糊C均值聚类与GWO-GAT-BiGRU模型的分布式光伏集群短期功率预测[J]. 中国电力, 2026, 59(9): 223−234.

ZHANG Duxi, ZHAO Wenyan, CHEN Chao, et al. Short-term power forecasting of distributed photovoltaic cluster based on fuzzy C-means clustering and GWO-GAT-BiGRU model[J]. 2026, 59(9): 223-234.
章杜锡, 赵文巘, 陈超, 等. 基于模糊C均值聚类与GWO-GAT-BiGRU模型的分布式光伏集群短期功率预测[J]. 中国电力, 2026, 59(9): 223−234. DOI： 10.11930/j.issn.1004-9649.202510068

ZHANG Duxi, ZHAO Wenyan, CHEN Chao, et al. Short-term power forecasting of distributed photovoltaic cluster based on fuzzy C-means clustering and GWO-GAT-BiGRU model[J]. 2026, 59(9): 223-234. DOI： 10.11930/j.issn.1004-9649.202510068.

摘要

在新型电力系统转型的背景下，光伏等可再生能源逐渐成为供电主体，其出力精准预测是保障电力系统稳定运行的关键。现有光伏功率预测方法多侧重时序序列建模，忽略了集群内部的空间特征。为此，提出一种融合模糊C均值（fuzzy C-means，FCM）聚类与时空图神经网络的分布式光伏集群短期功率预测模型。模型首先基于FCM算法对光伏集群进行聚类划分并构建图结构，再利用图注意力网络提取空间关联特征，结合双向门控循环单元捕捉时序特征；同时引入灰狼优化算法优化模型超参数。基于10个分布式光伏电站实际数据进行算例分析，结果表明，所提方法通过融合光伏集群内部的空间特征与时序特征，预测精度与其他模型相比至少提高了6.7%。

Abstract

With the transition to the new power system

renewable energy sources

particularly photovoltaic (PV) generation

have become the dominant power supplier. Accurate forecasting of PV output is critical for maintaining power system stability. However

most existing forecasting methods have mainly focused on temporal sequence modeling while overlooking the spatial feature information among PV clusters. To address this issue

this study proposes a distributed PV short-term forecasting model that integrates fuzzy C-means (FCM) clustering and spatiotemporal graph neural network. Firstly

distributed PV stations are clustered with the FCM algorithm

and then a graph structure is constructed. The graph attention network (GAT) is employed to capture spatial features among distributed PV stations

and the sequences are processed by the bidirectional gated recurrent unit (BiGRU) to extract temporal features. In addition

the grey wolf optimizer (GWO) is introduced to optimize the model hyperparameters. The experimental results based on real data from 10 distributed PV stations verify that the proposed forecasting method effectively integrates spatial and temporal features of distributed PV stations and improves the prediction accuracy by at least 6.7% compared with other models.

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