基于RF特征提取和TCN-BiGRU模型的短期光伏发电功率预测

刘毅力; 陈园园

doi:10.19912/j.0254-0096.tynxb.2024-0440

PDF(2962 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (7) : 682-689. DOI: 10.19912/j.0254-0096.tynxb.2024-0440

第二十七届中国科协年会学术论文

基于RF特征提取和TCN-BiGRU模型的短期光伏发电功率预测

刘毅力, 陈园园

作者信息 +

SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON RF FEATURE EXTRACTION AND TCN-BiGRU MODEL

Liu Yili, Chen Yuanyuan

Author information +

文章历史 +

摘要

为解决目前光伏发电功率预测模型输入数据冗余和单一模型预测精度不高的问题,构建一种分季节基于随机森林（RF）进行特征提取的时序卷积网络（TCN）、双向门控单元循环网络（BiGRU）和缩放点积注意力机制（SDA）结合的短期光伏发电功率预测模型。首先,采用RF计算各气象特征对发电功率的贡献度以选取关键特征;然后,将关键气象特征和原始功率数据用于结合SDA机制的TCN-BiGRU组合模型进行预测;最后,根据实际算例对所提组合模型进行验证。结果表明,提出的组合模型与其他模型相比具有更高的预测精度。

Abstract

To solve the issues of input data redundancy and low prediction accuracy of single models in current photovoltaic power forecasting, a short-term PV power prediction model based on seasonal random forests （RF） feature extraction based on temporal convolutional network （TCN）, bidirectional gated recurrent unit network （BiGRU） and scaled-dot product attention mechanism （SDA） was constructed. Firstly, RF is employed to evaluate the contribution of each meteorological feature to power generation to select key features. Then, the key meteorological features and raw power data are imput into the TCN-BiGRU model combined with SDA mechanism. Finally, the proposed combination model is verified according to a practical example. The results demonstrate better prediction accuracy compared to other existing models.

导出引用

刘毅力, 陈园园. 基于RF特征提取和TCN-BiGRU模型的短期光伏发电功率预测[J]. 太阳能学报. 2025, 46(7): 682-689 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0440

Liu Yili, Chen Yuanyuan. SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON RF FEATURE EXTRACTION AND TCN-BiGRU MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 682-689 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0440

中图分类号： TM615 TP18

参考文献

[1] 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.
[2] 闫钇汛, 王丽婕, 郭洪武, 等. 基于多特征分析和提取的短期光伏功率预测[J]. 高电压技术, 2022, 48(9): 3734-3743.
YAN Y X, WANG L J, GUO H W, et al.Short-term photovoltaic power prediction based on multi-feature analysis and extraction[J]. High voltage engineering, 2022, 48(9): 3734-3743.
[3] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
LAI C W, LI J H, CHEN B, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217.
[4] 李薇, 王鑫鹏, 许野, 等. 基于传递闭包的光伏短期功率组合预测方法研究[J]. 太阳能学报, 2023, 44(6): 265-274.
LI W, WANG X P, XU Y, et al.Research of combined forecasting method of short-term photovoltaic power on transitive closure based[J]. Acta energiae solaris sinica, 2023, 44(6): 265-274.
[5] 吴珺玥, 赵二刚, 郭增良, 等. 基于Spearman系数和TCN的光伏出力超短期多步预测[J]. 太阳能学报, 2023, 44(9): 180-186.
WU J Y, ZHAO E G, GUO Z L, et al.Ultra-short-term photovoltaic power multi-step prediction based on Spearman coefficient and TCN[J]. Acta energiae solaris sinica, 2023, 44(9): 180-186.
[6] 胡兵, 詹仲强, 陈洁, 等. 基于PCA-GA-Elman的短期光伏出力预测研究[J]. 太阳能学报, 2020, 41(6): 256-263.
HU B, ZHAN Z Q, CHEN J, et al.Prediction research on short-term photovoltaic output based on PCA-GA-Elman[J]. Acta energiae solaris sinica, 2020, 41(6): 256-263.
[7] 陈泽西. 基于新能源发电功率预测的储能系统优化配置研究[D]. 北京: 华北电力大学, 2022.
CHEN Z X.Research on optimal allocation of energy storage system based on power prediction of renewable energy generation[D]. Beijing: North China Electric Power University, 2022.
[8] 王涛, 王旭, 许野, 等. 计及相似日的LSTM光伏出力预测模型研究[J]. 太阳能学报, 2023, 44(8): 316-323.
WANG T, WANG X, XU Y, et al.Study on LSTM photovoltaic output prediction model considering similar days[J]. Acta energiae solaris sinica, 2023, 44(8): 316-323.
[9] 史加荣, 殷诏. 基于GRU-BLS的超短期光伏发电功率预测[J]. 智慧电力, 2023, 51(9): 38-45.
SHI J R, YIN Z.Prediction of ultra short term photovoltaic power generation based on GRU-BLS[J]. Smart power, 2023, 51(9): 38-45.
[10] 陈禹帆, 温蜜, 张凯, 等. 基于相似日匹配及TCN-Attention的短期光伏出力预测[J]. 电测与仪表, 2022, 59(10): 108-116.
CHEN Y F, WEN M, ZHANG K, et al.Short-term photovoltaic output forecasting based on similar day matching and TCN-Attention[J]. Electrical measurement & instrumentation, 2022, 59(10): 108-116.
[11] 吴家葆, 曾国辉, 张振华, 等. 基于K-means分层聚类的TCN-GRU和LSTM动态组合光伏短期功率预测[J]. 可再生能源, 2023, 41(8): 1015-1022.
WU J B, ZENG G H, ZHANG Z H, et al.Dynamic combination of TCN-GRU and LSTM photovoltaic short-term power prediction based on K-means hierarchical clustering[J]. Renewable energy resources, 2023, 41(8): 1015-1022.
[12] 梁宏涛, 王莹, 刘红菊, 等. 基于注意力机制的CNN-BiGRU短期光伏发电功率预测[J]. 计算机测量与控制, 2022, 30(6): 259-265.
LIANG H T, WANG Y, LIU H J, et al.Short-term PV output forecast of BiGRU based on the attention mechanism[J]. Computer measurement & control, 2022, 30(6): 259-265.
[13] 吉兴全,赵国航,叶平峰,等.基于QMD-HBiGRU的短期光伏功率预测方法[J].高电压技术, 2024, 50(9): 3850-3859.
JI X Q, ZHAO G H, YE P F, et al.Short-term PV forecasting method based on QMD-HBiGRU model[J]. High voltage engineering, 2024, 50(9): 3850-3859.
[14] CHAI W W, QIN L, DONG H Y, et al.Short-term load prediction based on the combination of K-means and random forest[J]. Journal of physics: conference series, 2022, 2166(1): 012027.
[15] YANG G, DU S H, DUAN Q L, et al.Short-term demand forecasting method in power markets based on the KSVM-TCN-GBRT[J]. Computational intelligence and neuroscience, 2022, 2022: 6909558.
[16] 郝洁. 基于多元特征的风电功率预测与源荷不确定性经济调度研究[D]. 兰州: 兰州理工大学, 2023.
HAO J.Wind power prediction based on multivariate feature and economic scheduling with uncertainty of source and load[D]. Lanzhou: Lanzhou University of Technology, 2023.
[17] 徐钽,谢开贵,王宇,等.基于TCN-Wpsformer混合模型的超短期风电功率预测[J].电力自动化设备,2024,44(8):54-61.
XU T, XU K G, WANG Y, et al.Ultra-short-term wind power forecasting based on TCN-Wpsformer hybrid model[J]. Electric power automation equipment, 2024, 44(8):54-61.
[18] CHEN K, LAGHROUCHE S, DJERDIR A.Degradation prediction of proton exchange membrane fuel cell based on grey neural network model and particle swarm optimization[J]. Energy conversion and management, 2019, 195: 810-818.
[19] SUN B, SUN T, ZHANG Y J, et al.Urban traffic flow online prediction based on multi-component attention mechanism[J]. IET intelligent transport systems, 2020, 14(10): 1249-1258.
[20] 贾婧, 王庆生, 陈永乐, 等. 基于注意力机制的DDoS攻击检测方法[J]. 计算机工程与设计, 2021, 42(9): 2439-2445.
JIA J, WANG Q S, CHEN Y L, et al.Detection method of DDoS attack based on attention mechanism[J]. Computer engineering and design, 2021, 42(9): 2439-2445.
[21] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[C]//31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach: Curran Associates, 2017: 5998-6008.