SHORT- AND MEDIUM-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON IMPROVED VMD AND CSABO-TCN-BiGRU

Mao Tingrui; Li Peiqiang

doi:10.19912/j.0254-0096.tynxb.2024-1446

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 168-177. DOI: 10.19912/j.0254-0096.tynxb.2024-1446

SHORT- AND MEDIUM-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON IMPROVED VMD AND CSABO-TCN-BiGRU

Mao Tingrui¹, Li Peiqiang²

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Abstract

An improved variational mode decomposition （VMD） algorithm based on modal correlation and reconstruction error, and an improved subtraction-average-based optimizer （CSABO） to optimize short- and medium-term photovoltaic （PV） power prediction model consisting of temporal convolutional network （TCN） and bidirectional gated recurrent unit （BiGRU） are proposed. Firstly, the historical PV data are decomposed into multiple components with different frequencies using the improved VMD. Then, the components are combined with key meteorological factors, and the PV power forecasts are reconstructed by the TCN-BiGRU model by forecasting each time series data separately. Finally, the parameters of the prediction model are optimized using CSABO to improve the model performance. The actual Australian PV data is used as an arithmetic example for experimental analysis, and the results demonstrate that the proposed model exhibits the best evaluation indexes and higher prediction accuracy compared with EMD-TCN-BiGRU, CEEMDAN-TCN-BiGRU and VMD-CNN-LSTM models.

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photovoltaic power / prediction models / variational mode decomposition / subtraction-average-based optimizer / temporal convolutional network / bidirectional gated recurrent unit

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Mao Tingrui, Li Peiqiang. SHORT- AND MEDIUM-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON IMPROVED VMD AND CSABO-TCN-BiGRU[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 168-177 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1446

References

[1] 国家能源局. 2024年光伏发电建设情况[EB/OL]. (2025-01-27). https://www.nea.gov.cn/20250221/f04452701c914d51a89d0c0ea6f4acd1/c.html.
National Energy Administration. Photovoltaic power generation construction status in2024[EB/OL]. (2025-01-27). https://www.nea.gov.cn/20250221/f04452701c914d51a89d0c0ea6f4acd1/c.html.
[2] 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.
[3] 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 the QMD-HBiGRU model[J]. High voltage engineering, 2024, 50(9): 3850-3859.
[4] 陈炜, 艾欣, 吴涛, 等. 光伏并网发电系统对电网的影响研究综述[J]. 电力自动化设备, 2013, 33(2): 26-32, 39.
CHEN W, AI X, WU T, et al.Influence of grid-connected photovoltaic system on power network[J]. Electric power automation equipment, 2013, 33(2): 26-32, 39.
[5] 陈龙, 张菁, 张昊立, 等. 基于VMD和射箭算法优化改进ELM的短期光伏发电预测[J]. 太阳能学报, 2023, 44(10): 135-141.
CHEN L, ZHANG J, ZHANG H L, et al.Short-term photovoltaic power generation forecast based on VMD-IAA-IHEKLM model[J]. Acta energiae solaris sinica, 2023, 44(10): 135-141.
[6] 孟安波, 陈顺, 王陈恩, 等. 基于混沌CSO优化时序注意力GRU模型的超短期风电功率预测[J]. 电网技术, 2021, 45(12): 4692-4700.
MENG A B, CHEN S, WANG C E, et al.Ultra-short-term wind power prediction based on chaotic CSO optimized temporal attention GRU model[J]. Power system technology, 2021, 45(12): 4692-4700.
[7] 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.
[8] 荆博, 谭伦农, 钱政, 等. 光伏发电短期预测研究进展综述[J]. 电测与仪表, 2017, 54(12): 1-6.
JING B, TAN L N, QIAN Z, et al.An overview of research progress of short-term photovoltaic forecasts[J]. Electrical measurement & instrumentation, 2017, 54(12): 1-6.
[9] 牛东晓, 纪会争. 风电功率物理预测模型引入误差量化分析方法[J]. 电力系统自动化, 2020, 44(8): 57-65.
NIU D X, JI H Z.Quantitative analysis method for errors introduced by physical prediction model of wind power[J]. Automation of electric power systems, 2020, 44(8): 57-65.
[10] 刘杰, 陈雪梅, 陆超, 等. 基于数据统计特性考虑误差修正的两阶段光伏功率预测[J]. 电网技术, 2020, 44(8): 2891-2897.
LIU J, CHEN X M, LU C, et al.Two-stage photovoltaic power forecasting and error correction method based on statistical characteristics of data[J]. Power system technology, 2020, 44(8): 2891-2897.
[11] 骆钊, 吴谕侯, 朱家祥, 等. 基于多尺度时间序列块自编码Transformer神经网络模型的风电超短期功率预测[J]. 电网技术, 2023, 47(9): 3527-3537.
LUO Z, WU Y H, ZHU J X, et al.Wind power forecasting based on multi-scale time series block auto-encoder transformer neural network model[J]. Power system technology, 2023, 47(9): 3527-3537.
[12] 郭占伍, 张泽亚, 周兴华, 等. 考虑气象因素的电采暖负荷预测研究[J]. 电测与仪表, 2022, 59(2): 154-158.
GUO Z W, ZHANG Z Y, ZHOU X H, et al.Study on forecasting method of electric heating load considering meteorological factors[J]. Electrical measurement & instrumentation, 2022, 59(2): 154-158.
[13] 王清亮, 杨博, 应欣峰, 等. 非晴空条件下光伏发电短期功率预测方法[J]. 太阳能学报, 2022, 43(3): 188-196.
WANG Q L, YANG B, YING X F, et al.Short-term photovoltaic power forecasting method under non-clear sky condition[J]. Acta energiae solaris sinica, 2022, 43(3): 188-196.
[14] 张静, 褚晓红, 黄学安, 等. 一种基于加权马尔科夫链修正的SVM光伏出力预测模型[J]. 电力系统保护与控制, 2019, 47(19): 63-68.
ZHANG J, CHU X H, HUANG X A, et al.A model for photovoltaic output prediction based on SVM modified by weighted Markov chain[J]. Power system protection and control, 2019, 47(19): 63-68.
[15] 苗长新, 李昊, 王霞, 等. 基于数据驱动和深度学习的超短期风电功率预测[J]. 电力系统自动化, 2021, 45(14): 22-29.
MIAO C X, LI H, WANG X, et al.Data-driven and deep-learning-based ultra-short-term wind power prediction[J]. Automation of electric power systems, 2021, 45(14): 22-29.
[16] 樊磊, 张倩, 李国丽, 等. 基于长短期记忆网络数字孪生体的短期光伏发电预测[J]. 现代电力, 2023, 40(6): 899-905.
FAN L, ZHANG Q, LI G L, et al.Short-term photovoltaic power generation prediction based on LSTM digital twins[J]. Modern electric power, 2023, 40(6): 899-905.
[17] 崔佳豪, 毕利. 基于混合神经网络的光伏电量预测模型的研究[J]. 电力系统保护与控制, 2021, 49(13): 142-149.
CUI J H, BI L.Research on photovoltaic power forecasting model based on hybrid neural network[J]. Power system protection and control, 2021, 49(13): 142-149.
[18] 赵倩, 黄景涛. 基于EMD-SA-SVR的超短期风电功率预测研究[J]. 电力系统保护与控制, 2020, 48(4): 89-96.
ZHAO Q, HUANG J T.On ultra-short-term wind power prediction based on EMD-SA-SVR[J]. Power system protection and control, 2020, 48(4): 89-96.
[19] 赵凌云, 刘友波, 沈晓东, 等. 基于CEEMDAN和改进时间卷积网络的短期风电功率预测模型[J]. 电力系统保护与控制, 2022, 50(1): 42-50.
ZHAO L Y, LIU Y B, SHEN X D, et al.Short-TERM wind power prediction model based on CEEMDAN and an improved time convolutional network[J]. Power system protection and control, 2022, 50(1): 42-50.
[20] 陈臣鹏, 赵鑫, 毕贵红, 等. 基于多模式分解和麻雀优化残差网络的短期风速预测模型[J]. 电网技术, 2022, 46(8): 2975-2985.
CHEN C P, ZHAO X, BI G H, et al.SSA-res-GRU short-term wind speed prediction model based on multi-model decomposition[J]. Power system technology, 2022, 46(8): 2975-2985.
[21] 王粟, 江鑫, 曾亮, 等. 基于VMD-DESN-MSGP模型的超短期光伏功率预测[J]. 电网技术, 2020, 44(3): 917-926.
WANG S, JIANG X, ZENG L, et al.Ultra-short-term photovoltaic power prediction based on VMD-DESN-MSGP model[J]. Power system technology, 2020, 44(3): 917-926.
[22] 杨晶显, 张帅, 刘继春, 等. 基于VMD和双重注意力机制LSTM的短期光伏功率预测[J]. 电力系统自动化, 2021, 45(3): 174-182.
YANG J X, ZHANG S, LIU J C, et al.Short-term photovoltaic power prediction based on variational mode decomposition and long short term memory with dual-stage attention mechanism[J]. Automation of electric power systems, 2021, 45(3): 174-182.
[23] TROJOVSKÝ P, DEHGHANI M.Subtraction-average-based optimizer: a new swarm-inspired metaheuristic algorithm for solving optimization problems[J]. Biomimetics, 2023, 8(2): 149.
[24] 崔明勇, 董文韬, 卢志刚. 基于密度聚类模态分解的卷积神经网络和长短期记忆网络短期风电功率预测[J]. 现代电力, 2024, 41(4): 631-641.
CUI M Y, DONG W T, LU Z G.CEEMDAN-CNN-LSTM short-term wind power prediction based on density clustering[J]. Modern electric power, 2024, 41(4): 631-641.