Day-ahead generation power forecasting for wind-photovoltaic clusters considering complementary generation characteristics

JI Ling; JI Shunguo; WANG Tao; LEI Wenjing; XU Qing; WANG Weijie; ZHENG Yuyang; XU Fei; CHEN Lei; HAO Ling

doi:10.11930/j.issn.1004-9649.202601003

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Day-ahead generation power forecasting for wind-photovoltaic clusters considering complementary generation characteristics

更新时间：2026-07-31

- Day-ahead generation power forecasting for wind-photovoltaic clusters considering complementary generation characteristics
- Vol. 59, Issue 7, Pages: 78-91(2026)
- 作者机构：
  
  1. 国电南京自动化股份有限公司
  2. 南京国电南自电网自动化有限公司
  3. 中国华电集团有限公司青海分公司
  4. 华电（海西）新能源有限公司
  5. 新型电力系统运行与控制全国重点实验室（清华大学）
  6. 清华大学电机工程与应用电子技术系
- 作者简介：
- 基金信息：
  
  This work is supported by the National Key Research and Development Program (No.2024YFB4206900)
- DOI：10.11930/j.issn.1004-9649.202601003
  CLC：
- Published：2026
- 稿件说明：
移动端阅览
纪陵, 季顺国, 王涛, 等. 考虑风光出力互补特性的风光集群日前发电功率预测[J]. 中国电力, 2026, 59(7): 78−91.

JI Ling, JI Shunguo, WANG Tao, et al. Day-ahead generation power forecasting for wind-photovoltaic clusters considering complementary generation characteristics[J]. 2026, 59(7): 78-91.
纪陵, 季顺国, 王涛, 等. 考虑风光出力互补特性的风光集群日前发电功率预测[J]. 中国电力, 2026, 59(7): 78−91. DOI： 10.11930/j.issn.1004-9649.202601003

JI Ling, JI Shunguo, WANG Tao, et al. Day-ahead generation power forecasting for wind-photovoltaic clusters considering complementary generation characteristics[J]. 2026, 59(7): 78-91. DOI： 10.11930/j.issn.1004-9649.202601003.

摘要

随着风光电站的规模化发展，开展准确的区域风光电站集群功率预测对区域电力系统的稳定性和可靠性至关重要。因此，提出了一种考虑出力互补特性的风光集群发电功率预测方法。首先构建了多维度风光互补特性量化评价指标体系，系统评估任意两风光电站之间的出力互补性；其次基于量化的不同发电形式电站出力互补性与相同发电形式电站出力相关性，构建基于图卷积网络（graph convolutional network，GCN）的风光集群日前功率预测模型；最后引入多任务学习机制联合优化不同季节场景的预测任务。仿真结果表明，电站风光出力互补特性和多任务学习机制的引入，有效提高了风光电站集群功率预测的准确性。

Abstract

With the large-scale development of wind and photovoltaic (PV) power stations

accurate power forecasting for regional wind-PV clusters is crucial for the stability and reliability of regional power systems. Therefore

this paper proposes a power forecasting method for wind-PV clusters considering output complementarity. Firstly

a multi-dimensional indicator system is established to quantify wind-PV complementarity

so as to systematically assess the output complementarity between any two wind and PV power stations. Secondly

based on the quantified output complementarity between plants of different generation types and the output correlation between plants of the same generation type

a graph convolutional network (GCN) based day-ahead power forecasting model is constructed for wind-PV clusters. Finally

a multi-task learning mechanism is introduced to jointly optimize forecasting tasks for different seasonal scenarios. Simulation results demonstrate that the integration of wind-PV complementarity and multi-task learning mechanism effectively improves the accuracy of power forecasting for wind-PV clusters.

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