基于聚类SABO-VMD和组合神经网络的短期光伏发电功率预测

冯建铭; 希望&#x000b7;阿不都瓦依提; 蔺红

doi:10.19912/j.0254-0096.tynxb.2023-1681

PDF(3343 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (2) : 357-366. DOI: 10.19912/j.0254-0096.tynxb.2023-1681

基于聚类SABO-VMD和组合神经网络的短期光伏发电功率预测

冯建铭, 希望·阿不都瓦依提, 蔺红

作者信息 +

SHORT-TERM PV POWER FORECASTING BASED ON CLUSTERING SABO-VMD AND ENSEMBLE NEURAL NETWORKS

Feng Jianming, Xiwang·Abuduwayiti, Lin Hong

Author information +

文章历史 +

摘要

针对光伏发电预测单一模型处于不同天气状况时预测精度不高等问题,建立以卷积神经网络（CNN）和双向长短期记忆网络（BiLSTM）为基础的组合神经网络模型。提出一种基于鱼鹰优化算法（OOA）,用以优化组合神经网络参数。此外引入注意力机制（Attention）突出强相关性因素的影响。采用高斯混合模型聚类（GMM）划分历史光伏数据为数个天气类型,并提出基于减法平均的优化算法（SABO）优化变分模态分解（VMD）参数,实现对各天气类型数据的分解。实验结果表明：基于SABO-VMD优化数据分解参数能有效提高预测精度;经实验对比分析,该文所提模型精度明显更高。

Abstract

To address the challenges of low prediction accuracy when using a single model for photovoltaic power generation forecasting under varying weather conditions, a composite neural network model is established based on a convolutional neural network and a bidirectional long short-term memory network. The osprey optimization algorithm is introduced to optimize the parameters of ensemble neural networks. Additionally, an attention mechanism is incorporated to emphasize the influence of strong correlation factors. We employ Gaussian mixture model clustering to categorize historical photovoltaic data into various weather types and propose a subtraction average-based optimizer algorithm to optimize variational mode decomposition for data decomposition based on different weather types. Experimental results demonstrate that optimizing data decomposition parameters using SABO-VMD effectively enhances prediction accuracy. In comparison to other combined prediction models,through experimental comparisons, the accuracy of the model proposed in this paper is significantly higher.

导出引用

冯建铭, 希望·阿不都瓦依提, 蔺红. 基于聚类SABO-VMD和组合神经网络的短期光伏发电功率预测[J]. 太阳能学报. 2025, 46(2): 357-366 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1681

Feng Jianming, Xiwang·Abuduwayiti, Lin Hong. SHORT-TERM PV POWER FORECASTING BASED ON CLUSTERING SABO-VMD AND ENSEMBLE NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 357-366 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1681

中图分类号： TM615

参考文献

[1] 李晖, 刘栋, 姚丹阳. 面向碳达峰碳中和目标的我国电力系统发展研判[J]. 中国电机工程学报, 2021, 41(18): 6245-6259.
LI H, LIU D, YAO D Y.Analysis and reflection on the development of power system towards the goal of carbon emission peak and carbon neutrality[J]. Proceedings of the CSEE, 2021, 41(18): 6245-6259.
[2] 任大伟, 侯金鸣, 肖晋宇, 等. 支撑双碳目标的新型储能发展潜力及路径研究[J]. 中国电力, 2023, 56(8): 17-25.
REN D W, HOU J M, XIAO J Y, et al.Research on development potential and path of new energy storage supporting carbon peak and carbon neutrality[J]. Electric power, 2023, 56(8): 17-25.
[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] 李倩倩, 严珂. 基于RGA-BiLSTM模型的太阳辐照度预测[J]. 中国计量大学学报, 2023, 34(1): 74-83.
LI Q Q, YAN K.Solar irradiance prediction based on the RGA-BiLSTM model[J]. Journal of China University of Metrology, 2023, 34(1): 74-83.
[5] 张建新, 刘俊星, 傅文珍, 等. 一个新的太阳电池显式模型以预测在任何太阳辐照度和温度时的I-V[J]. 太阳能学报, 2023, 44(4): 393-397.
ZHANG J X, LIU J X, FU W Z, et al.A new explicit model of solar cell to predict I-V at any irradiance and temperature condition[J]. Acta energiae solaris sinica, 2023, 44(4): 393-397.
[6] LIU Z G, YU H, JIN W.Adaptive leakage protection for low-voltage distribution systems based on SSA-BP neural network[J]. Applied sciences, 2023, 13(16): 9273.
[7] CAO W B, LIU Y P, MEI H W, et al.Short-term district power load self-prediction based on improved XGBoost model[J]. Engineering applications of artificial intelligence, 2023, 126: 106826.
[8] WANG X H, MENG R X, WANG G T, et al.The research on fault diagnosis of rolling bearing based on current signal CNN-SVM[J]. Measurement science and technology, 2023, 34(12): 125021.
[9] 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.
[10] 欧阳福莲, 王俊, 周杭霞. 基于改进迁移学习和多尺度CNN-BiLSTM-Attention的短期电力负荷预测方法[J]. 电力系统保护与控制, 2023, 51(2): 132-140.
OUYANG F L, WANG J, ZHOU H X.Short-term power load forecasting method based on improved hierarchical transfer learning and multi-scale CNN-BiLSTM-Attention[J]. Power system protection and control, 2023, 51(2): 132-140.
[11] 薛阳, 燕宇铖, 贾巍, 等. 基于改进灰狼算法优化长短期记忆网络的光伏功率预测[J]. 太阳能学报, 2023, 44(7): 207-213.
XUE Y, YAN Y C, JIA W, et al.Photovoltaic power prediction model based on IGWO-LSTM[J]. Acta energiae solaris sinica, 2023, 44(7): 207-213.
[12] 雷柯松, 吐松江·卡日, 伊力哈木·亚尔买买提, 等. 基于WGAN-GP和CNN-LSTM-Attention的短期光伏功率预测[J]. 电力系统保护与控制, 2023, 51(9): 108-118.
LEI K S, TUSONGJIANG·KARI,YILIHAMU·YAERMAIMAITI, et al. Prediction of short-term photovoltaic power based on WGAN-GP and CNN-LSTM-Attention[J]. Power system protection and control, 2023, 51(9): 108-118.
[13] 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.
[14] 汪正军, 高静方, 赵冰, 等. 基于风速预测的风电场虚拟惯量协调控制技术[J]. 太阳能学报, 2022, 43(10): 138-143.
WANG Z J, GAO J F, ZHAO B, et al.Wind farm virtual inertia coordinated control technology based on wind speed prediction[J]. Acta energiae solaris sinica, 2022, 43(10): 138-143.
[15] 王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103.
WANG F Z, WANG S F, ZHANG L.Ultra short term power prediction of photovoltaic power generation based on VMD-LSTM and error compensation[J]. Acta energiae solaris sinica, 2022, 43(8): 96-103.
[16] 滕陈源, 丁逸超, 张有兵, 等. 基于VMD-Informer-BiLSTM模型的超短期光伏功率预测[J]. 高电压技术, 2023, 49(7): 2961-2971.
TENG C Y, DING Y C, ZHANG Y B, et al.Ultra-short-term photovoltaic power prediction based on VMD-Informer-BiLSTM model[J]. High voltage engineering, 2023, 49(7): 2961-2971.
[17] 李宏扬, 高丙朋. 基于改进VMD和SNS-Attention-GRU的短期光伏发电功率预测[J]. 太阳能学报, 2023, 44(8): 292-300.
LI H Y, GAO B P.Short-term PV power forecasting based on improved VMD and SNS-Attention-GRU[J]. Acta energiae solaris sinica, 2023, 44(8): 292-300.
[18] NOWAKOWSKA E, KORONACKI J, LIPOVETSKY S.Clusterability assessment for Gaussian mixture models[J]. Applied mathematics and computation, 2015, 256: 591-601.
[19] DEHGHANI M, TROJOVSKÝ P.Osprey optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems[J]. Frontiers in mechanical engineering, 2023, 8: 1126450.
[20] TROJOVSKÝ P, DEHGHANI M.Subtraction-average-based optimizer: a new swarm-inspired metaheuristic algorithm for solving optimization problems[J]. Biomimetics, 2023, 8(2): 149.
[21] YANG M S, LAI C Y, LIN C Y.A robust EM clustering algorithm for Gaussian mixture models[J]. Pattern recognition, 2012, 45(11): 3950-3961.
[22] 王晓霞, 俞敏, 冀明, 等. 基于气候相似性与SSA-CNN-LSTM的光伏功率组合预测[J]. 太阳能学报, 2023, 44(6): 275-283.
WANG X X, YU M, JI M, et al.Photovoltaic power combination forecasting based on climate similarity and SSA-CNN-LSTM[J]. Acta energiae solaris sinica, 2023, 44(6): 275-283.