基于分季节相似日聚类和Stacking集成学习的超短期光伏功率预测

李忠文; 李海洋; 程志平; 随权; 王义

doi:10.19912/j.0254-0096.tynxb.2025-0002

PDF(3741 KB)

太阳能学报 ›› 2026, Vol. 47 ›› Issue (5) : 732-740. DOI: 10.19912/j.0254-0096.tynxb.2025-0002

基于分季节相似日聚类和Stacking集成学习的超短期光伏功率预测

李忠文, 李海洋, 程志平, 随权, 王义

作者信息 +

ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SEASONAL SIMILAR DAY CLUSTERING AND STACKING ENSEMBLE LEARNING

Li Zhongwen, Li Haiyang, Cheng Zhiping, Sui Quan, Wang Yi

Author information +

文章历史 +

摘要

为解决传统超短期光伏功率预测中单一预测模型稳定性欠佳、泛化能力受限等缺陷,提出一种基于分季节相似日聚类和Stacking集成学习的超短期光伏功率预测方法。首先,采用皮尔逊和斯皮尔曼相关系数筛选出光伏出力关键气象特征;其次,基于澳大利亚某光伏场站历史数据进行分季节K-均值相似日聚类,以便更准确地反映光伏发电的季节特性;然后,选择长短期记忆网络（LSTM）、卷积神经网络（CNN）、极限学习机（ELM）和支持向量机（SVM）作为基学习器,采用极限梯度提升（XGBoost）作为元学习器,并通过粒子群优化算法（PSO）对XGBoost的超参数进行优化,构建Stacking集成预测模型;最后,通过仿真实验证明该方法在不同季节的多种天气条件下表现出良好的预测性能和稳定性。

Abstract

Traditional ultra-short-term photovoltaic （PV） power forecasting methods often rely on a single prediction model, resulting in limited stability and generalization capability. To overcome these shortcomings, this study proposes a novel ultra-short-term PV power forecasting approach integrating seasonal similar-day clustering with Stacking ensemble learning. Key meteorological features relevant to PV output are first identified using Pearson and Spearman correlation analyses. Historical data from an Australian PV plant are then processed through seasonal K-means similar-day clustering to capture the seasonal characteristics of PV generation more accurately. Four diverse base learners—long short-term memory （LSTM） networks, convolutional neural networks （CNN）, extreme learning machine（ELM）, and support vector machine（SVM）—are combined, with extreme gradient boosting （XGBoost） serving as the meta-learner. The hyperparameters of XGBoost are optimized via the particle swarm optimization （PSO） algorithm to construct the final Stacking ensemble model. Simulation results verify that the proposed method delivers enhanced forecasting accuracy and stability under diverse seasonal and meteorological conditions.

导出引用

李忠文, 李海洋, 程志平, 随权, 王义. 基于分季节相似日聚类和Stacking集成学习的超短期光伏功率预测[J]. 太阳能学报. 2026, 47(5): 732-740 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0002

Li Zhongwen, Li Haiyang, Cheng Zhiping, Sui Quan, Wang Yi. ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON SEASONAL SIMILAR DAY CLUSTERING AND STACKING ENSEMBLE LEARNING[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 732-740 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0002

中图分类号： TM615

参考文献

[1] 周孝信, 赵强, 张玉琼, 等. “双碳”目标下我国能源电力系统发展趋势分析: 绿电替代与绿氢替代[J]. 中国电机工程学报, 2024, 44(17): 6707-6721.
ZHOU X X, ZHAO Q, ZHANG Y Q, et al.Analysis of the development trend of China's energy and power system under the dual carbon target: green electricity substitution and green hydrogen substitution[J]. Proceedings of the CSEE, 2024, 44(17): 6707-6721.
[2] GUPTA P, SINGH R.PV power forecasting based on data-driven models: a review[J]. International journal of sustainable engineering, 2021, 14(6): 1733-1755.
[3] 栗峰, 丁杰, 周才期, 等. 新型电力系统下分布式光伏规模化并网运行关键技术探讨[J]. 电网技术, 2024, 48(1): 184-199.
LI F, DING J, ZHOU C Q, et al.Key technologies of large-scale grid-connected operation of distributed photovoltaic under new-type power system[J]. Power system technology, 2024, 48(1): 184-199.
[4] DAS U K, TEY K S, SEYEDMAHMOUDIAN M, et al.Forecasting of photovoltaic power generation and model optimization: a review[J]. Renewable and sustainable energy reviews, 2018, 81: 912-928.
[5] 朱琼锋, 李家腾, 乔骥, 等. 人工智能技术在新能源功率预测的应用及展望[J]. 中国电机工程学报, 2023, 43(8): 3027-3048.
ZHU Q F, LI J T, QIAO J, et al.Application and prospect of artificial intelligence technology in renewable energy forecasting[J]. Proceedings of the CSEE, 2023, 43(8): 3027-3048.
[6] 张冬冬, 单琳珂, 刘天皓. 人工智能技术在风力与光伏发电数据挖掘及功率预测中的应用综述[J]. 综合智慧能源, 2025, 47(3): 32-46.
ZHANG D D, SHAN L K, LIU T H.Review on the application of artificial intelligence in data mining and wind and photovoltaic power forecasting[J]. Integrated intelligent energy, 2025, 47(3): 32-46.
[7] 韩晓, 王涛, 韦晓广, 等. 考虑阵列间时空相关性的超短期光伏出力预测[J]. 电力系统保护与控制, 2024, 52(14): 82-94.
HAN X, WANG T, WEI X G, et al.Ultrashort-term photovoltaic output forecasting considering spatiotemporal correlation between arrays[J]. Power system protection and control, 2024, 52(14): 82-94.
[8] 彭曙蓉, 陈慧霞, 孙万通, 等. 基于改进LSTM的光伏发电功率预测方法研究[J]. 太阳能学报, 2024, 45(11): 296-302.
PENG S R, CHEN H X, SUN W T, et al.Research on photovoitaic power prediction method based on improved LSTM[J]. Acta energiae solaris sinica, 2024, 45(11): 296-302.
[9] HUANG Q, WEI S Y.Improved quantile convolutional neural network with two-stage training for daily-ahead probabilistic forecasting of photovoltaic power[J]. Energy conversion and management, 2020, 220: 113085.
[10] 王琦, 季顺祥, 钱子伟, 等. 基于熵理论和改进ELM的光伏发电功率预测[J]. 太阳能学报, 2020, 41(10): 151-158.
WANG Q, JI S X, QIAN Z W, et al.Photovoltaic power prediction based on entropy theory and improved ELM[J]. Acta energiae solaris sinica, 2020, 41(10): 151-158.
[11] PAN M Z, LI C, GAO R, et al.Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization[J]. Journal of cleaner production, 2020, 277: 123948.
[12] 贾凌云, 云斯宁, 赵泽妮, 等. 神经网络短期光伏发电预测的应用研究进展[J]. 太阳能学报, 2022, 43(12): 88-97.
JIA L Y, YUN S N, ZHAO Z N, et al.Recent progress of short-term forecasting of photovoltaic generation based on artificial neural networks[J]. Acta energiae solaris sinica, 2022, 43(12): 88-97.
[13] 武新章, 王泽宇, 代伟, 等. 基于异质聚类与Stacking的双集成光伏发电功率预测[J]. 电网技术, 2023, 47(1): 275-283.
WU X Z, WANG Z Y, DAI W, et al.Bi-ensembled photovoltaic (PV) power prediction based on heterogeneous clustering and stacking[J]. Power system technology, 2023, 47(1): 275-283.
[14] 杨国清, 张凯, 王德意, 等. 基于包络线聚类的多模融合超短期光伏功率预测算法[J]. 电力自动化设备, 2021, 41(2): 39-46.
YANG G Q, ZHANG K, WANG D Y, et al.Multi-mode fusion ultra-short-term photovoltaic power prediction algorithm based on envelope clustering[J]. Electric power automation equipment, 2021, 41(2): 39-46.
[15] 董俊, 刘瑞, 束洪春, 等. 基于BIRCH聚类的L-Transformer分布式光伏短期发电功率预测[J]. 高电压技术, 2024, 50(9): 3883-3893.
DONG J, LIU R, SHU H C, et al.Short-term distributed photovoltaic power generation prediction based on BIRCH cluster-ing and L-Transformer[J]. High voltage engineering, 2024, 50(9): 3883-3893.
[16] WU T, HU R F, ZHU H Y, et al.Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition[J]. Energy, 2024, 288: 129770.
[17] ZHANG M Y, HAN Y, WANG C Y, et al.Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: a case study using DKASC data[J]. Applied energy, 2024, 375: 124085.
[18] 王献志, 曾四鸣, 周雪青, 等. 基于XGBoost联合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(4): 236-242.
WANG X Z, ZENG S M, ZHOU X Q, et al.Power forecast of photovoltaic generation based on XGBoost combined model[J]. Acta energiae solaris sinica, 2022, 43(4): 236-242.
[19] 史佳琪, 张建华. 基于多模型融合Stacking集成学习方式的负荷预测方法[J]. 中国电机工程学报, 2019, 39(14): 4032-4042.
SHI J Q, ZHANG J H.Load forecasting based on multi-model by Stacking ensemble learning[J]. Proceedings of the CSEE, 2019, 39(14): 4032-4042.
[20] 李永飞, 张耀, 林帆, 等. 基于气候特征分析及改进XGBoost算法的中长期光伏电站发电量预测方法[J]. 电力系统保护与控制, 2024, 52(11): 84-92.
LI Y F, ZHANG Y, LIN F, et al.Medium-and long-term power generation forecast based on climate characterisation and an improved XGBoost algorithm for photovoltaic power plants[J]. Power system protection and control, 2024, 52(11): 84-92.