SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON PIA DATA REPAIRING AND CLUSTERING USINGMVMD-CNN-BILSTM-ATT

Zhao Jingjing; Sheng Jie; Wang Han; Zhou Ruikang; Fan Hong

doi:10.19912/j.0254-0096.tynxb.2024-0857

PDF(1299 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 547-554. DOI: 10.19912/j.0254-0096.tynxb.2024-0857

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON PIA DATA REPAIRING AND CLUSTERING USINGMVMD-CNN-BILSTM-ATT

Zhao Jingjing, Sheng Jie, Wang Han, Zhou Ruikang, Fan Hong

Author information +

History +

Abstract

To improve the accuracy of photovoltaic （PV） power prediction,a hybrid short-term photovoltaic power prediction model based on the Pearson interpolation algorithm （PIA）,fuzzy C-means clustering algorithm （FCM）,multivariate variational mode decomposition （MVMD）,convolutional neural network （CNN）,bidirectional long short-term memory network （BiLSTM）,and attention mechanism （ATTENTION） is proposed.Firstly,the PIA is utilized to repair the missing data in the dataset. Secondly,the FCM algorithm clusters historical data into sunny,cloudy,and rainy days. Then,the MVMD is employed to decompose the PV power into several intrinsic mode functions （IMFs）.Subsequently,a combination of CNN and BiLSTM networks is used to fully extract the features of each IMF,with the Attention Mechanism introduced to emphasize and weigh important information. Finally, the forecasting of each IMF is conducted,and the predicted values are summed to obtain the final prediction result. Compared with other PV power forecasting models,the proposed hybrid model demonstrates superior forecasting accuracy.

Key words

photovoltaic power / prediction model / multivariate variational mode decomposition / fuzzy clustering / bidirectional long short-term memory neural network / attention mechanism

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhao Jingjing, Sheng Jie, Wang Han, Zhou Ruikang, Fan Hong. SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON PIA DATA REPAIRING AND CLUSTERING USINGMVMD-CNN-BILSTM-ATT[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 547-554 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0857

References

[1] 张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819.
ZHANG Z G, KANG C Q. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
[2] 张童彦, 廖清芬, 唐飞, 等. 基于气象资源插值与迁移学习的广域分布式光伏功率预测方法[J]. 中国电机工程学报, 2023, 43(20): 7929-7940.
ZHANG T Y, LIAO Q F, TANG F, et al. Wide-area distributed photovoltaic power forecast method based on meteorological resource interpolation and transfer learning[J]. Proceedings of the CSEE, 2023, 43(20): 7929-7940.
[3] 黄博阳, 何肖蒙, 肖小兵, 等. 基于FCM-WS-BP的光伏日前出力预测研究[J]. 控制工程, 2023, 30(12): 2254-2260.
HUANG B Y, HE X M, XIAO X B, et al. Research of photovoltaic day-ahead output power prediction based on FCM-WS-BP[J]. Control engineering of China, 2023, 30(12): 2254-2260.
[4] 刘兴华, 耿晨, 谢胜寒, 等. 考虑光伏发电不确定性的日前火电-光伏经济调度[J]. 系统仿真学报, 2022, 34(8): 1874-1884.
LIU X H, GENG C, XIE S H, et al. Day ahead thermal-photovoltaic economic dispatch considering uncertainty of photovoltaic power generation[J]. Journal of system simulation, 2022, 34(8): 1874-1884.
[5] 李争, 张杰, 徐若思, 等. 基于相似日聚类和PCC-VMD-SSA-KELM模型的短期光伏功率预测[J]. 太阳能学报, 2024, 45(2): 460-468.
LI Z, ZHANG J, XU R S, et al.Short term photovoltaic power prediction based on similar day clustering and PCC-VMD-SSA-KELM model[J]. Acta energiae solaris sinica, 2024, 45(2): 460-468.
[6] 王仲平, 何黎黎, 丁更乾. 基于EMD-LSTM-ARMA模型短期发电量组合预测[J]. 现代电子技术, 2023, 46(3): 151-155.
WANG Z P, HE L L, DING G Q. Short-term power generation combination prediction based on EMD-LSTM-ARMA model[J]. Modern electronics technique, 2023, 46(3): 151-155.
[7] 王瑞, 高强, 逯静. 基于CEEMDAN-LSSVM-ARIMA模型的短期光伏功率预测[J]. 传感器与微系统, 2022, 41(5): 118-122.
WANG R, GAO Q, LU J. Short-term photovoltaic power prediction based on CEEMDAN-LSSVM-ARIMA model[J]. Transducer and microsystem technologies, 2022, 41(5): 118-122.
[8] 杨延勇, 孟祥剑, 高峰, 等. 一种基于双层人工神经网络的多时间尺度区域光伏功率预测方法[J]. 华北电力大学学报(自然科学版), 2021, 48(2): 55-63.
YANG Y Y, MENG X J, GAO F, et al. A multi-time scale regional PV power forecasting method based on double-layer artificial neural network[J]. Journal of North China Electric Power University(natural science edition), 2021, 48(2): 55-63.
[9] 吴汉斌, 时珉, 郑焕坤, 等. 基于EEMD-ALOCO-SVM模型的光伏功率短期区间预测[J]. 太阳能学报, 2023, 44(11): 64-71.
WU H B, SHI M, ZHENG H K, et al. Short-term interval prediction of photovoltaic power based on EEMD-ALOCO-SVM model[J]. Acta energiae solaris sinica, 2023, 44(11): 64-71.
[10] LIMOUNI T, YAAGOUBI R, BOUZIANE K, et al.Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model[J]. Renewable energy, 2023, 205: 1010-1024.
[11] HOSSAIN M S, MAHMOOD H.Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast[J]. IEEE access, 2020, 8: 172524-172533.
[12] AMIRI A F, KICHOU S, OUDIRA H, et al.Fault detection and diagnosis of a photovoltaic system based on deep learning using the combination of a convolutional neural network(CNN) and bidirectional gated recurrent unit (Bi-GRU)[J]. Sustainability, 2024, 16(3): 1012.
[13] JIA P Y, ZHANG H B, LIU X M, et al.Short-term photovoltaic power forecasting based on VMD and ISSA-GRU[J]. IEEE access, 2021, 9: 105939-105950.
[14] 金伟勇, 卢丽娜, 赖欢欢, 等. 基于功率特征的K-ISSA-LSTM光伏功率预测[J]. 太阳能学报, 2024, 45(2): 429-434.
JIN W Y, LU L N, LAI H H, et al. K-ISSA-LSTM photovoltaic power prediction based on power characteristic[J]. Acta energiae solaris sinica, 2024, 45(2): 429-434.
[15] 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.
[16] 焦丕华, 蔡旭, 王乐乐, 等. 考虑数据分解和进化捕食策略的BiLSTM短期光伏发电功率预测[J]. 太阳能学报, 2024, 45(2): 435-442.
JIAO P H, CAI X, WANG L L, et al.BiLSTM short-term photovoltaic power prediction considering data decomposition and evolutionary predation strategies[J]. Acta energiae solaris sinica, 2024, 45(2): 435-442.
[17] 何威, 苏中元, 史金林, 等. 基于双重注意力GRU与相似修正的光伏功率预测[J]. 太阳能学报, 2024, 45(3): 480-487.
HE W, SU Z Y, SHI J L, et al. Photovoltaic power forecasting based on dual-attention-GRU and similarity modification[J]. Acta energiae solaris sinica, 2024, 45(3): 480-487.
[18] 纪德洋, 金锋, 冬雷, 等. 基于皮尔逊相关系数的光伏电站数据修复[J]. 中国电机工程学报, 2022, 42(4): 1514-1523.
JI D Y, JIN F, DONG L, et al.Data repairing of photovoltaic power plant based on Pearson correlation coefficient[J]. Proceedings of the CSEE, 2022, 42(4): 1514-1523.