PV POWER INTERVAL PREDICTION BASED ON STL DECOMPOSITION AND TPA MECHANISM

Li Yihang; Xiao Hui; Yi Chun; Long Feiyu

doi:10.19912/j.0254-0096.tynxb.2023-2052

PDF(1767 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 22-29. DOI: 10.19912/j.0254-0096.tynxb.2023-2052

PV POWER INTERVAL PREDICTION BASED ON STL DECOMPOSITION AND TPA MECHANISM

Li Yihang, Xiao Hui, Yi Chun, Long Feiyu

Author information +

History +

Abstract

Aiming at the problem that the PV power point prediction contains insufficient information to provide a sufficient basis for grid scheduling, a PV interval prediction method based on STL decomposition and temporal pattern attention mechanism is proposed. Firstly, the original PV power sequence is decomposed by STL to obtain subsequences： trend term, seasonal term and residual term. Then, the trend term is predicted by extreme learning machine （ELM）; the seasonal term and the residual term are predicted by bi-directional gated recurrent unit （BiGRU） based on temporal pattern attention （TPA）. Finally, the interval prediction is obtained by quantile regression, and the PV output interval prediction is obtained by superposing the two outputs. The proposed method is validated by the point prediction results and interval prediction results on the PV output dataset of a certain place in Hunan province.

Key words

photovoltaic power generation / power prediction / neural network / quantile regression / bidirectional gated cyclic unit network

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Li Yihang, Xiao Hui, Yi Chun, Long Feiyu. PV POWER INTERVAL PREDICTION BASED ON STL DECOMPOSITION AND TPA MECHANISM[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 22-29 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2052

References

[1] 张沈习, 王丹阳, 程浩忠, 等. 双碳目标下低碳综合能源系统规划关键技术及挑战[J]. 电力系统自动化, 2022, 46(8): 189-207.
ZHANG S X, WANG D Y, CHENG H Z, et al.Key technologies and challenges of low-carbon integrated energy system planning for carbon emission peak and carbon neutrality[J]. Automation of electric power systems, 2022, 46(8): 189-207.
[2] 万灿, 宋永华. 新能源电力系统概率预测理论与方法及其应用[J]. 电力系统自动化, 2021, 45(1): 2-16.
WAN C, SONG Y H.Theories, methodologies and applications of probabilistic forecasting for power systems with renewable energy sources[J]. Automation of electric power systems, 2021, 45(1): 2-16.
[3] 邓惟绩, 肖辉, 李金泽, 等. 基于改进长短期记忆网络和高斯过程回归的光伏功率预测方法[J]. 电器与能效管理技术, 2021(8): 51-57.
DENG W J, XIAO H, LI J Z, et al.Photovoltaic power prediction method based on improved long short term memory network and Gaussian process regression[J]. Electrical & energy management technology, 2021(8): 51-57.
[4] FATEMI S A, KUH A, FRIPP M.Parametric methods for probabilistic forecasting of solar irradiance[J]. Renewable energy, 2018, 129: 666-676.
[5] ZIADI Z, OSHIRO M, SENJYU T, et al.Optimal voltage control using inverters interfaced with PV systems considering forecast error in a distribution system[J]. IEEE transactions on sustainable energy, 2014, 5(2): 682-690.
[6] 刘翼飞, 崔承刚. 基于地基云图云量特征的光伏发电功率区间预测[J]. 南方电网技术, 2023, 17(2): 92-100.
LIU Y F, CUI C G.Interval prediction for short-term solar power based on cloud features of ground-based cloud images[J]. Southern power system technology, 2023, 17(2): 92-100.
[7] LOTFI M, JAVADI M, OSÓRIO G J, et al. A novel ensemble algorithm for solar power forecasting based on kernel density estimation[J]. Energies, 2020, 13(1): 216.
[8] 张亚刚, 赵云鹏, 王思祺. 基于EVMD和布谷鸟算法的短期风功率区间预测[J]. 太阳能学报, 2022, 43(8): 292-299.
ZHANG Y G, ZHAO Y P, WANG S Q.Short term wind power interval prediction based on EVMD and cuckoo algorithm[J]. Acta energiae solaris sinica, 2022, 43(8): 292-299.
[9] 贾德香, 吕干云, 林芬, 等. 基于SAPSO-BP和分位数回归的光伏功率区间预测[J]. 电力系统保护与控制, 2021, 49(10): 20-26.
JIA D X, LYU G Y, LIN F, et al.Photovoltaic power interval prediction based on SAPSO-BP and quantile regression[J]. Power system protection and control, 2021, 49(10): 20-26.
[10] 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.
[11] WEN Y X, ALHAKEEM D, MANDAL P, et al.Performance evaluation of probabilistic methods based on bootstrap and quantile regression to quantify PV power point forecast uncertainty[J]. IEEE transactions on neural networks and learning systems, 2020, 31(4): 1134-1144.
[12] YAMAMOTO H, KONDOH J, KODAIRA D.Assessing the impact of features on probabilistic modeling of photovoltaic power generation[J]. Energies, 2022, 15(15): 5337.
[13] 李伟, 王冰, 曹智杰, 等. 基于混沌理论的鸡群改进算法及其在风电功率区间预测中的应用[J]. 太阳能学报, 2021, 42(7): 350-358.
LI W, WANG B, CAO Z J, et al.Application of CCSO in wind power interval prediction[J]. Acta energiae solaris sinica, 2021, 42(7): 350-358.
[14] 罗世坚. 基于CIWOA-LSTM网络的短期光伏功率预测及其不确定性分析[D]. 南宁: 广西大学, 2022.
LUO S J.Short-term photovoltaic power prediction based on CIWOA-LSTM network and its uncertainty analysis[D]. Nanning: Guangxi University, 2022.
[15] 赵耀, 高少炜, 李东东, 等. 基于天气相似聚类与QRNN的短期光伏功率区间概率预测[J]. 电力系统自动化, 2023, 47(23): 152-161.
ZHAO Y, GAO S W, LI D D, et al.Short-term interval probability prediction of photovoltaic power based on weather similarity clustering and quantile regression neural network[J]. Automation of electric power systems, 2023, 47(23): 152-161.
[16] 丰胜成, 郭继成, 付华, 等. 基于ISSA与GRU分位数回归的风电功率概率密度预测[J]. 电工电能新技术, 2023, 42(10): 55-65.
FENG S C, GUO J C, FU H, et al.Wind power probability density prediction based on ISSA and GRU quantile regression[J]. Advanced technology of electrical engineering and energy, 2023, 42(10): 55-65.
[17] 马志侠, 张林鍹, 邱朝洁, 等. 基于CEEMD-SSA-LSTM的园区综合能源系统两阶段优化调度[J]. 高电压技术, 2023, 49(4): 1430-1440.
MA Z X, ZHANG L X, QIU C J, et al.Two-stage optimal scheduling of the park integrated energy system based on CEEMD-SSA-LSTM[J]. High voltage engineering, 2023, 49(4): 1430-1440.
[18] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[19] 商立群, 李洪波, 侯亚东, 等. 基于VMD-ISSA-KELM的短期光伏发电功率预测[J]. 电力系统保护与控制, 2022, 50(21): 138-148.
SHANG L Q, LI H B, HOU Y D, et al.Short-term photovoltaic power generation prediction based on VMD-ISSA-KELM[J]. Power system protection and control, 2022, 50(21): 138-148.
[20] 赵征, 周孜钰, 南宏钢. 基于VMD的CNN-BiLSTM超短期风电功率多步区间预测[J]. 华北电力大学学报(自然科学版), 2022, 49(4): 91-97.
ZHAO Z, ZHOU Z Y, NAN H G.Research on multi-step interval forecasting of CNN-BiLSTM ultra-short-term wind power based on VMD[J]. Journal of North China Electric Power University (natural science edition), 2022, 49(4): 91-97.
[21] ZHOU H X, WANG J, OUYANG F L, et al.A two-stage method for ultra-short-term PV power forecasting based on data-driven[J]. IEEE access, 2023, 11: 41175-41189.
[22] 林鹏, 于洋, 张昕, 等. 基于CRITIC加权和IMFO-LSSVM的光伏发电功率短期预测[J]. 电气自动化, 2021, 43(6): 13-16.
LIN P, YU Y, ZHANG X, et al.Short-term prediction of photovoltaic generation power based on CRITIC weighting and IMFO-LSSVM[J]. Electrical automation, 2021, 43(6): 13-16.
[23] 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.
[24] HEIDARI A, KHOVALYG D.Short-term energy use prediction of solar-assisted water heating system: application case of combined attention-based LSTM and time-series decomposition[J]. Solar energy, 2020, 207: 626-639.
KOENKER R, HALLOCK KF, Quantile regression[J]. Jonrnal of economic perspectives, 2001, 15(4): 143-156.
[25] KOENKER R, HALLOCK K F.Quantile regression[J]. Journal of economic perspectives, 2001, 15(4): 143-156.
[26] HUANG G B, ZHU Q Y, SIEW C K.Extreme learning machine: a new learning scheme of feedforward neural networks[C]//2004 IEEE International Joint Conference on Neural Networks. IEEE, 2004, 2: 985-990.
[27] HU H Y, WANG Q, CHENG M, et al.Trajectory prediction neural network and model interpretation based on temporal pattern attention[J]. IEEE transactions on intelligent transportation systems, 2023, 24(3): 2746-2759.