基于IMVMD和BiLSTM-SARIMA组合模型的台区光伏短期发电功率预测

李承皓; 杨永标; 宋嘉启; 张翔颖; 徐青山

doi:10.19912/j.0254-0096.tynxb.2023-1492

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (2) : 433-440. DOI: 10.19912/j.0254-0096.tynxb.2023-1492

基于IMVMD和BiLSTM-SARIMA组合模型的台区光伏短期发电功率预测

李承皓^1,2, 杨永标^1,2, 宋嘉启³, 张翔颖⁴, 徐青山^1,2

作者信息 +

SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON IMVMD AND BILSTM-SARIMA COMBINATION MODEL IN STATION AREA

Li Chenghao^1,2, Yang Yongbiao^1,2, Song Jiaqi³, Zhang Xiangying⁴, Xu Qingshan^1,2

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摘要

针对台区分布式光伏短期发电功率预测精度低的难题,提出一种基于增强型鲸鱼优化算法的多元变分模态分解方法,并结合反向传播神经网络耦合双向长短期记忆网络和季节性差分自回归滑动平均的组合模型,实现台区分布式光伏短期发电功率预测。首先对鲸鱼优化算法的收敛因子、权重等进行改进,然后用它去优化多元变分模态分解方法中的通道数量和惩罚因子,得到最佳分解效果的参数值。再针对与外界气象等因素强相关的光伏发电功率时间序列数据,利用改进多元模态分解将序列最优分解。将分解后的各模态分量输入到单独构建的双向长短期记忆网络和季节性差分自回归滑动平均模型中,获取分量预测值,两个模型得到的分量预测值分别叠加得到各自的完整预测结果。将它们分别乘以权重后相加即为最终预测结果,权重通过反向传播神经网络进行修正。仿真结果说明相比于其他方法,所提模型能有效提高光伏短期发电的预测精度。

Abstract

Aiming at the difficulty of low accuracy of short-term power prediction of distributed PV in the station area, a multivariate variational modal decomposition approach based on the enhanced whale optimization algorithm is proposed and combined with a back propagation neural network coupled with bidirectional long and short-term memory network and a seasonal differential autoregressive sliding average is combined to achieve the prediction of short-term power generation of distributed PV in the station area. In the paper, the convergence factor and weight of the whale optimization algorithm are firstly improved, and then it is used to optimize the number of channels and the penalty factor in the multivariate variational modal decomposition method, to get the parameter values of the best decomposition effect. Then for the PV power time series data which is strongly correlated with external meteorological factors, the sequence is optimally decomposed using the improved multivariate modal decomposition. The decomposed modal components are input into the separately constructed bidirectional long- and short-term memory network and seasonal differential autoregressive sliding average model to obtain the component prediction values, and the component prediction values obtained from the two models are superimposed to obtain their respective complete prediction results. The final prediction result is obtained

导出引用

李承皓, 杨永标, 宋嘉启, 张翔颖, 徐青山. 基于IMVMD和BiLSTM-SARIMA组合模型的台区光伏短期发电功率预测[J]. 太阳能学报. 2025, 46(2): 433-440 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1492

Li Chenghao, Yang Yongbiao, Song Jiaqi, Zhang Xiangying, Xu Qingshan. SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON IMVMD AND BILSTM-SARIMA COMBINATION MODEL IN STATION AREA[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 433-440 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1492

中图分类号： TM615

参考文献

[1] 成珂, 孙琦琦, 马晓瑶. 基于主成分回归分析的气象因子对光伏发电量的影响[J]. 太阳能学报, 2021, 42(2): 403-409.
CHENG K, SUN Q Q, MA X Y.Influence of meteorological factors on photovoltaic power generation based on principal component regression analysis[J]. Acta energiae solaris sinica, 2021, 42(2): 403-409.
[2] AHMED R, SREERAM V, MISHRA Y, et al.A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization[J]. Renewable and sustainable energy reviews, 2020, 124: 109792.
[3] 舒胜, 谢应明, 杨文宇, 等. 光伏发电预测方法研究进展[J]. 热能动力工程, 2020, 35(11): 1-11.
SHU S, XIE Y M, YANG W Y, et al.A review of photovoltaic power generation forecasting methods[J]. Journal of engineering for thermal energy and power, 2020, 35(11): 1-11.
[4] 张惠娟, 刘琪, 岑泽尧, 等. 基于GWO-MLP的光伏系统输出功率短期预测模型[J]. 电测与仪表, 2022, 59(7): 72-77, 113.
ZHANG H J, LIU Q, CEN Z Y, et al.Short-term prediction model of output power of photovoltaic system based on GWO-MLP[J]. Electrical measurement & instrumentation, 2022, 59(7): 72-77, 113.
[5] SOBRI S, KOOHI-KAMALI S, RAHIM N A.Solar photovoltaic generation forecasting methods: a review[J]. Energy conversion and management, 2018, 156: 459-497.
[6] 谢小瑜, 周俊煌, 张勇军, 等. 基于W-BiLSTM的可再生能源超短期发电功率预测方法[J]. 电力系统自动化, 2021, 45(8): 175-184.
XIE X Y, ZHOU J H, ZHANG Y J, et al.W-BiLSTM based ultra-short-term generation power prediction method of renewable energy[J]. Automation of electric power systems, 2021, 45(8): 175-184.
[7] VAGROPOULOS S I, CHOULIARAS G I, KARDAKOS E G, et al.Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting[C]//2016 IEEE International Energy Conference(ENERGYCON), Leuven, Belgium, 2016.
[8] 刘永超. 融合变分模态分解的深度神经网络预测研究及应用[D]. 长春: 长春工业大学, 2022.
LIU Y C.Research and application of deep neural network prediction fused with variational mode decomposition[D]. Changchun: Changchun University of Technology, 2022.
[9] GUO T T, ZHANG T P, LIM E, et al.A review of wavelet analysis and its applications: challenges and opportunities[J]. IEEE access, 2022, 10: 58869-58903.
[10] HOSSAIN M R, ISMAIL M T, BIN ABDUL KARIM S A. Improving stock price prediction using combining forecasts methods[J]. IEEE access, 2021, 9: 132319-132328.
[11] 李凯丰, 王浩全, 侯清. 基于小波分析联合VMD的超声信号去噪方法研究[J]. 中国测试, 2023, 49(4): 52-59.
LI K F, WANG H Q, HOU Q.Research on ultrasonic signal denoising method based on wavelet analysis combined with VMD[J]. China measurement & test, 2023, 49(4): 52-59.
[12] MIRJALILI S, LEWIS A.The whale optimization algorithm[J]. Advances in engineering software, 2016, 95: 51-67.
[13] 李畸勇, 张伟斌, 赵新哲, 等. 改进鲸鱼算法优化支持向量回归的光伏最大功率点跟踪[J]. 电工技术学报, 2021, 36(9): 1771-1781.
LI J Y, ZHANG W B, ZHAO X Z, et al.Global maximum power point tracking for PV array based on support vector regression optimized by improved whale algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1771-1781.
[14] 武泽权, 牟永敏. 一种改进的鲸鱼优化算法[J]. 计算机应用研究, 2020, 37(12): 3618-3621.
WU Z Q, MU Y M.Improved whale optimization algorithm[J]. Application research of computers, 2020, 37(12): 3618-3621.
[15] LI W W, SHI Q, SIBTAIN M, et al.A hybrid forecasting model for short-term power load based on sample entropy, two-phase decomposition and whale algorithm optimized support vector regression[J]. IEEE access, 2020, 8: 166907-166921.
[16] 陈斌, 王俊江, 赵明胤, 等. 基于改进鲸鱼优化算法的光伏发电系统MPPT控制研究[J]. 电力系统及其自动化学报, 2023, 35(2): 19-26.
CHEN B, WANG J J, ZHAO M Y, et al.Research on MPPT control of photovoltaic power generation system based on improved whale optimization algorithm[J]. Proceedings of the CSU-EPSA, 2023, 35(2): 19-26.
[17] KAUR G, ARORA S.Chaotic whale optimization algorithm[J]. Journal of computational design and engineering, 2018, 5(3): 275-284.
[18] 李玲玲, 王鑫, 郎永波, 等. 基于改进鲸鱼算法的微网复合储能系统容量优化配置[J]. 电测与仪表, 2019, 56(16): 104-110.
LI L L, WANG X, LANG Y B, et al.Capacity configuration of micro-grid composite energy storage system based on improved whale optimization algorithm[J]. Electrical measurement & instrumentation, 2019, 56(16): 104-110.
[19] 郭威, 张凯, 魏新杰, 等. 高渗透率分布式光伏接入的新型电力系统净功率预测[J]. 电测与仪表, 2022, 59(12): 48-55.
GUO W, ZHANG K, WEI X J, et al.Net power prediction for a novel power system with high permeability distributed photovoltaic access[J]. Electrical measurement & instrumentation, 2022, 59(12): 48-55.
[20] 凌立文, 张大斌. 组合预测模型构建方法及其应用研究综述[J]. 统计与决策, 2019, 35(1): 18-23.
LING L W, ZHANG D B.A review of construction and application of combination forecast model[J]. Statistics & decision, 2019, 35(1): 18-23.
[21] ALKAWAZ A N, ABDELLATIF A, KANESAN J, et al.Day-ahead electricity price forecasting based on hybrid regression model[J]. IEEE access, 2022, 10: 108021-108033.
[22] VON KRANNICHFELDT L, WANG Y, ZUFFEREY T, et al.Online ensemble approach for probabilistic wind power forecasting[J]. IEEE transactions on sustainable energy, 2022, 13(2): 1221-1233.
[23] 马磊, 黄伟, 李克成, 等. 基于Attention-LSTM的光伏超短期功率预测模型[J]. 电测与仪表, 2021, 58(2): 146-152.
MA L, HUANG W, LI K C, et al.Photovoltaic ultra-short-term power prediction model based on Attention-LSTM[J]. Electrical measurement & instrumentation, 2021, 58(2): 146-152.
[24] 王俊杰, 毕利, 张凯, 等. 基于多特征融合和XGBoost-LightGBM-ConvLSTM的短期光伏发电量预测[J]. 太阳能学报, 2023, 44(7): 168-174.
WANG J J, BI L, ZHANG K, et al.Short-term photovoltaic power generation prediction based on multi-feature fusion and XGBoost-LightGBM-ConvLSTM[J]. Acta energiae solaris sinica, 2023, 44(7): 168-174.
[25] 彭曙蓉, 郑国栋, 黄士峻, 等. 基于XGBoost算法融合多特征短期光伏发电量预测[J]. 电测与仪表, 2020, 57(24): 76-83.
PENG S R, ZHENG G D, HUANG S J, et al.Multiple-feature short-term photovoltaic generation forecasting based on XGBoost algorithm[J]. Electrical measurement & instrumentation, 2020, 57(24): 76-83.