RESEARCH ON PHOTOVOLTAIC OUTPUT PREDICTION METHOD OF TCN-TRANSFORMER CONSIDERING INGO AND VMD

Zheng Feifan; Xu Ye; Wang Xu; Meng Yikang; Li Zhongyan

doi:10.19912/j.0254-0096.tynxb.2025-0026

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 741-754. DOI: 10.19912/j.0254-0096.tynxb.2025-0026

RESEARCH ON PHOTOVOLTAIC OUTPUT PREDICTION METHOD OF TCN-TRANSFORMER CONSIDERING INGO AND VMD

Zheng Feifan¹, Xu Ye², Wang Xu², Meng Yikang², Li Zhongyan³

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Abstract

This paper proposes a combined prediction model for PV output through incorporating the improved northern goshawk optimization（INGO） algorithm, variational mode decomposition（VMD） technology and temporal convolutional network（TCN）- Transformer into a general framework. Firstly, the dynamic time warping （DTW） algorithm is employed to calculate the cumulative distance between the meteorological sequences of the predicted day and candidate historical days, leading to a set of historical days with similar weather characteristics to the targeted day. Secondly, the northern goshawk optimization （NGO） algorithm is improved with the aid of Logistic chaotic mapping, hierarchical mechanism, Levy method and nonlinear factor, effectively addressing the traditional NGO's susceptibility to local optima. Thirdly, the combination of INGO and VMD is used to decompose the original power generation sequence of the target day into different intrinsic mode functions （IMFs）. Finally, the meteorological data and IMF components are used as input and output variables, respectively, to train a combined TCN-Transformer prediction model optimized by INGO. Using the measured operational data from a PV power station in Yunnan Province as a case study, the superiority of the proposed method is validated. achieving high prediction accuracy under both sunny and cloudy weather conditions.

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photovoltaic power generation / prediction models / optimization algorithms / variational mode decomposition / temporal convolutional network / Transformer model

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Zheng Feifan, Xu Ye, Wang Xu, Meng Yikang, Li Zhongyan. RESEARCH ON PHOTOVOLTAIC OUTPUT PREDICTION METHOD OF TCN-TRANSFORMER CONSIDERING INGO AND VMD[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 741-754 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0026

References

[1] 舒印彪, 张丽英, 张运洲, 等. 我国电力碳达峰、碳中和路径研究[J]. 中国工程科学, 2021, 23(6): 1-14.
SHU Y B, ZHANG L Y, ZHANG Y Z, et al.Carbon peak and carbon neutrality path for China's power industry[J]. Strategic study of CAE, 2021, 23(6): 1-14.
[2] 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): 2806-2818.
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.
[3] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
LAI C W, LI J H, CHEN B, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217.
[4] 廖文宇, 郭鹏, 赵冰, 等. 基于小波分解与ARMA-BayesLSTM组合模型的风向短期预测[J]. 电力科学与工程, 2023, 39(5): 17-23.
LIAO W Y, GUO P, ZHAO B, et al.Short-term prediction of wind direction based on wavelet decomposition and ARMA-BayesLSTM combined model[J]. Electric power science and engineering, 2023, 39(5): 17-23.
[5] 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.
[6] 吴硕. 光伏发电系统功率预测方法研究综述[J]. 热能动力工程, 2021, 36(8): 1-7.
WU S.Review of power forecasting methods of photovoltaic power generation system[J]. Journal of engineering for thermal energy and power, 2021, 36(8): 1-7.
[7] 董存, 王铮, 白捷予, 等. 光伏发电功率超短期预测方法综述[J]. 高电压技术, 2023, 49(7): 2938-2951.
DONG C, WANG Z, BAI J Y, et al.Review of ultra-short-term forecasting methods for photovoltaic power generation[J]. High voltage engineering, 2023, 49(7): 2938-2951.
[8] 刘国海, 孙文卿, 吴振飞, 等. 基于Attention-GRU的短期光伏发电功率预测[J]. 太阳能学报, 2022, 43(2): 226-232.
LIU G H, SUN W Q, WU Z F, et al.Short-term photovoltaic power forecasting based on Attention-GRU model[J]. Acta energiae solaris sinica, 2022, 43(2): 226-232.
[9] 秦宇, 许野, 王鑫鹏, 等. 基于改进FCM-LSTM的光伏出力短期预测研究[J]. 太阳能学报, 2024, 45(8): 304-313.
QIN Y, XU Y, WANG X P, et al.Study on short-term photovoltaic output prediction based on improved FCM-LSTM[J]. Acta energiae solaris sinica, 2024, 45(8): 304-313.
[10] 谭海旺, 杨启亮, 邢建春, 等. 基于XGBoost-LSTM组合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(8): 75-81.
TAN H W, YANG Q L, XING J C, et al.Photovoltaic power prediction based on combined XGBoost-LSTM model[J]. Acta energiae solaris sinica, 2022, 43(8): 75-81.
[11] 王东风, 刘婧, 黄宇, 等. 结合太阳辐射量计算与CNN-LSTM组合的光伏功率预测方法研究[J]. 太阳能学报, 2024, 45(2): 443-450.
WANG D F, LIU J, HUANG Y, et al.Photovoltaic power prediction method combinating solar radiation calculation and CNN-LSTM[J]. Acta energiae solaris sinica, 2024, 45(2): 443-450.
[12] CHEN Y J, XIAO J W, WANG Y W, et al.Regional wind-photovoltaic combined power generation forecasting based on a novel multi-task learning framework and TPA-LSTM[J]. Energy conversion and management, 2023, 297: 117715.
[13] AGGA A, ABBOU A, LABBADI M, et al.Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models[J]. Renewable energy, 2021, 177: 101-112.
[14] 吴家葆, 曾国辉, 张振华, 等. 基于K-means分层聚类的TCN-GRU和LSTM动态组合光伏短期功率预测[J]. 可再生能源, 2023, 41(8): 1015-1022.
WU J B, ZENG G H, ZHANG Z H, et al.Dynamic combination of TCN-GRU and LSTM photovoltaic short-term power prediction based on K-means hierarchical clustering[J]. Renewable energy resources, 2023, 41(8): 1015-1022.
[15] 时珉, 许可, 王珏, 等. 基于灰色关联分析和GeoMAN模型的光伏发电功率短期预测[J]. 电工技术学报, 2021, 36(11): 2298-2305.
SHI M, XU K, WANG J, et al.Short-term photovoltaic power forecast based on grey relational analysis and GeoMAN model[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2298-2305.
[16] 孟亦康, 许野, 王鑫鹏, 等. 基于相似日选取和PCA-LSTM的光伏出力组合预测模型研究[J]. 太阳能学报, 2024, 45(7): 453-461.
MENG Y K, XU Y, WANG X P, et al.Research on photovoltaic output combination prediction model based on similar day selection and PCA-LSTM[J]. Acta energiae solaris sinica, 2024, 45(7): 453-461.
[17] 张宇华, 时鑫洋, 颜楠楠, 等. 逆向云灰色关联相似日的EEMD-RL-GWO-LSTM区域风光功率短期预测[J]. 太阳能学报, 2024, 45(10): 144-152.
ZHANG Y H, SHI X Y, YAN N N, et al.Short-term prediction of regional wind-solar power of EEMD-RL-GWO-LSTM on reverse cloud grey correlation similar days[J]. Acta energiae solaris sinica, 2024, 45(10): 144-152.
[18] LI P T, ZHOU K L, LU X H, et al.A hybrid deep learning model for short-term PV power forecasting[J]. Applied energy, 2020, 259: 114216.
[19] GAO B X, HUANG X Q, SHI J S, et al.Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks[J]. Renewable energy, 2020, 162: 1665-1683.
[20] 王涛, 李薇, 许野, 等. 计及相似日的VMD-FE-LSTM光伏出力组合预测模型研究[J]. 太阳能学报, 2024, 45(5): 490-499.
WANG T, LI W, XU Y, et al.Study on photovoltaic power prediction of VMD-FE-LSTM considering similar days[J]. Acta energiae solaris sinica, 2024, 45(5): 490-499.
[21] 周建国, 周路明, 王剑宇, 等. 基于优化分解与误差修正的光伏发电功率预测[J]. 太阳能学报, 2024, 45(7): 502-509.
ZHOU J G, ZHOU L M, WANG J Y, et al.PV power forecasting based on optimal decomposition and error correction[J]. Acta energiae solaris sinica, 2024, 45(7): 502-509.
[22] ZHEN H, NIU D X, WANG K K, et al.Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information[J]. Energy, 2021, 231: 120908.
[23] YAPRAKDAL F, YıLMAZ M B, BAYSAL M, et al. A deep neural network-assisted approach to enhance short-term optimal operational scheduling of a microgrid[J]. Sustainability, 2020, 12(4):1653.
[24] ZHOU N R, ZHOU Y, GONG L H, et al.Accurate prediction of photovoltaic power output based on long short-term memory network[J]. IET optoelectronics, 2020, 14(6): 399-405.
[25] 王瑞, 张璐婷, 逯静. 基于新型相似日选取和VMD-NGO-BiGRU的短期光伏功率预测[J]. 湖南大学学报(自然科学版), 2024, 51(2): 68-80.
WANG R, ZHANG L T, LU J.Short term photovoltaic power prediction based on new similar day selection and VMD-NGO-BiGRU[J]. Journal of Hunan University (natural sciences), 2024, 51(2): 68-80.
[26] DEHGHANI M, HUBÁLOVSKÝ Š, TROJOVSKÝ P. Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems[J]. IEEE access, 2021, 9: 162059-162080.
[27] 戴朝辉, 陈昊, 刘莘轶, 等. 基于K-medoids-GBDT-PSO-LSTM组合模型的短期光伏功率预测[J]. 太阳能学报, 2025, 46(1): 654-661.
DAI C H, CHEN H, LIU S Y, et al.Short-term photovoltaic power prediction based on K-medoids-GBDT-PSO-LSTM combined model[J]. Acta energiae solaris sinica, 2025, 46(1): 654-661.
[28] KIM J, OBREGON J, PARK H, et al.Multi-step photovoltaic power forecasting using transformer and recurrent neural networks[J]. Renewable and sustainable energy reviews, 2024, 200: 114479.
[29] 常智慧,徐耀松.基于CEEMDAN与INGO优化BiLSTM的短期电力负荷预测[J].控制工程, 2026, 33(2): 343-351.
CHANG Z H, XU Y S.Short-term power load forecasting based on CEEMDAN and INGO optimization BiLSTM[J]. Control engineering of China, 2026, 33(2): 343-351.