PV POWER FORECASTING BASED ON OPTIMAL DECOMPOSITION AND ERROR CORRECTION

Zhou Jianguo, Zhou Luming, Wang Jianyu, Qin Yuan, Wang Chongyu, Cai Chenhao

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (7) : 502-509.

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (7) : 502-509. DOI: 10.19912/j.0254-0096.tynxb.2023-0463

PV POWER FORECASTING BASED ON OPTIMAL DECOMPOSITION AND ERROR CORRECTION

  • Zhou Jianguo, Zhou Luming, Wang Jianyu, Qin Yuan, Wang Chongyu, Cai Chenhao
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Abstract

Aiming at the problem of low accuracy of photovoltaic power generation, the paper proposes a model based on optimized decomposition, noise reduction and error correction. The model is divided into three stages.In the first stage, the global search-based whale optimization algorithm(GSWOA) is used to select the parameters of the variational modal decomposition(VMD),and then the optimized VMD is used to decompose the original data; then the high-frequency components are reconstructed using mutual correlation analysis, and finally wavelet soft-threshold noise reduction(WTSD) is applied to the high-frequency components; in the second stage, a gated recurrent unit(GRU) is used to predict each component, and the initial prediction results are obtained by superposing all the component prediction results; in the third stage, the initial prediction results are subjected to error correction(EC). In order to verify the effectiveness of the model, the paper utilizes the measured PV data from Ningxia Sun Mountain PV power station in January, April, July and October 2021 to conduct experiments, and the experimental results show that the hybrid model exhibits better performance than LSTM, GRU, and VMD-LSTM.

Key words

photovoltaic power generation / power prediction / variational modal decomposition / gated recurrent unit / whale optimization algorithm based on global search / wavelet soft thresholding / error correction

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Zhou Jianguo, Zhou Luming, Wang Jianyu, Qin Yuan, Wang Chongyu, Cai Chenhao. PV POWER FORECASTING BASED ON OPTIMAL DECOMPOSITION AND ERROR CORRECTION[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 502-509 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0463

References

[1] FAN W Y, HAO Y.An empirical research on the relationship amongst renewable energy consumption, economic growth and foreign direct investment in China[J]. Renewable energy, 2020, 146: 598-609.
[2] HAO D N, QI L F, TAIRAB A M, et al.Solar energy harvesting technologies for PV self-powered applications: a comprehensive review[J]. Renewable energy, 2022, 188: 678-697.
[3] UFA R A, MALKOVA Y Y, RUDNIK V E, et al.A review on distributed generation impacts on electric power system[J]. International journal of hydrogen energy, 2022, 47(47): 20347-20361.
[4] RAHIMI N, PARK S, CHOI W, et al.A comprehensive review on ensemble solar power forecasting algorithms[J]. Journal of electrical engineering & technology, 2023, 18(2): 719-733.
[5] LIU W, REN C, XU Y.PV generation forecasting with missing input data: a super-resolution perception approach[J]. IEEE transactions on sustainable energy, 2021, 12(2): 1493-1496.
[6] KHAN I, ZHU H L, YAO J X, et al.Photovoltaic power forecasting based on Elman Neural Network software engineering method[C]//2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). Beijing, China, 2017: 747-750.
[7] LIU H, LONG Z H, DUAN Z, et al.A new model using multiple feature clustering and neural networks for forecasting hourly PM2.5 concentrations, and its applications in China[J]. Engineering, 2020, 6(8): 944-956.
[8] 刘丹, 刘方, 许彦平. 基于MIV-PSO-BPNN的光伏出力短期预测[J]. 太阳能学报, 2022, 43(6): 94-98.
LIU D, LIU F, XU Y P.Short-term photovoltaic power forecasting based on MIV-PSO-BPNN model[J]. Acta energiae solaris sinica, 2022, 43(6): 94-98.
[9] 杨帆, 申亚, 李东东, 等. 基于GA-GNNM的极地光伏发电功率预测方法[J]. 太阳能学报, 2022, 43(4): 167-174.
YANG F, SHEN Y, LI D D, et al.Polar photovoltaic power forecasting method based on GA-GNNM[J]. Acta energiae solaris sinica, 2022, 43(4): 167-174.
[10] ZHANG N, WANG S X, LIU G C, et al.All-factor short-term photovoltaic output power forecast[J]. IET renewable power generation, 2022, 16(1): 148-158.
[11] DU P D, ZHANG G, LI P L, et al.The photovoltaic output prediction based on variational mode decomposition and maximum relevance minimum redundancy[J]. Applied sciences, 2019, 9(17): 3593.
[12] ZHANG Y G, ZHANG J H, YU L Y, et al.A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique[J]. Energy, 2022, 254: 124378.
[13] 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.
[14] XU H Y, CHANG Y Q, ZHAO Y, et al.A hybrid model for multi-step wind speed forecasting based on secondary decomposition, deep learning, and error correction algorithms[J]. Journal of intelligent & fuzzy systems, 2021, 41(2): 3443-3462.
[15] LUO H Y, WANG D Y, YUE C Q, et al.Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting[J]. Atmospheric research, 2018, 201: 34-45.
[16] 刘磊, 白克强, 但志宏, 等. 一种全局搜索策略的鲸鱼优化算法[J]. 小型微型计算机系统, 2020, 41(9): 1820-1825.
LIU L, BAI K Q, DAN Z H, et al.Whale optimization algorithm with global search strategy[J]. Journal of Chinese computer systems, 2020, 41(9): 1820-1825.
[17] 王俊, 李霞, 周昔东, 等. 基于VMD和LSTM的超短期风速预测[J]. 电力系统保护与控制, 2020, 48(11): 45-52.
WANG J, LI X, ZHOU X D, et al.Ultra-short-term wind speed prediction based on VMD-LSTM[J]. Power system protection and control, 2020, 48(11): 45-52.
[18] ZHANG X Q, DUAN B S, HE S Y, et al.A new precipitation forecast method based on CEEMD-WTD-GRU[J]. Water supply, 2022, 22(4): 4120-4132.
[19] 王清亮, 代一凡, 王旭东, 等. 基于ICEEMDAN-LSTM-BNN的短期光伏发电功率概率预测[J]. 西安科技大学学报, 2023, 43(3): 593-602.
WANG Q L, DAI Y F, WANG X D, et al.Probabilistic prediction of short-term photovoltaic power based on ICEEMDAN-LSTM-BNN[J]. Journal of Xi'an University of Science and Technology, 2023, 43(3): 593-602.
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