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Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning

Author

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  • Gong, Jianqiang
  • Qu, Zhiguo
  • Zhu, Zhenle
  • Xu, Hongtao

Abstract

Accurate forecasting of photovoltaic (PV) generation can mitigate the impact of weather stochasticity on power systems and facilitate the development of effective energy scheduling strategies. This study presents a parallel TimesNet-bidirectional long short-term memory (PA-TimesNet-BiLSTM) model for ultra-short-term PV power forecasting. Initially, a seasonal trend decomposition using loess (STL) method was employed to decompose the raw data and reconstruct the input features. Subsequently, the TimesNet model extracts multiple periodic features in a two-dimensional space, while the BiLSTM model addresses long-term data dependencies. The PA-TimesNet-BiLSTM model hyperparameters are optimized using an asynchronous successive halving algorithm. The evaluation utilized standard metrics and Diebold–Mariano testing to assess the predictive performance of 12 benchmark models across two datasets. The results demonstrate the competitive performance of the PA-TimesNet-BiLSTM model. STL decomposition significantly benefits PV power forecasting. On the Australian dataset, the mean absolute error (MAE) and root mean square error (RMSE) of the PA-TimesNet-BiLSTM model improved by 7.42% and 4.31%, respectively, compared to the serial TimesNet-BiLSTM model. The STL-PA-TimesNet-BiLSTM achieved reductions of 29.05% and 33.01% in MAE and RMSE, respectively, compared with the PA-TimesNet-BiLSTM model. The PA-TimesNet-BiLSTM model effectively captured multidimensional periodic data features, enhancing its applicability to diverse prediction tasks.

Suggested Citation

  • Gong, Jianqiang & Qu, Zhiguo & Zhu, Zhenle & Xu, Hongtao, 2025. "Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009284
    DOI: 10.1016/j.energy.2025.135286
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    1. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Shang, Yuping & Sang, Shenghu & Tiwari, Aviral Kumar & Khan, Salahuddin & Zhao, Xin, 2024. "Impacts of renewable energy on climate risk: A global perspective for energy transition in a climate adaptation framework," Applied Energy, Elsevier, vol. 362(C).
    3. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    4. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    5. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    6. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    7. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    8. Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
    9. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    10. Dai, Yeming & Yu, Weijie & Leng, Mingming, 2024. "A hybrid ensemble optimized BiGRU method for short-term photovoltaic generation forecasting," Energy, Elsevier, vol. 299(C).
    11. Cao, Yisheng & Liu, Gang & Luo, Donghua & Bavirisetti, Durga Prasad & Xiao, Gang, 2023. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model," Energy, Elsevier, vol. 283(C).
    12. Zhao, He & Huang, Xiaoqiao & Xiao, Zenan & Shi, Haoyuan & Li, Chengli & Tai, Yonghang, 2024. "Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks," Renewable Energy, Elsevier, vol. 220(C).
    13. Wang, Xinyu & Ma, Wenping, 2024. "A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 295(C).
    14. Kim, Jimin & Obregon, Josue & Park, Hoonseok & Jung, Jae-Yoon, 2024. "Multi-step photovoltaic power forecasting using transformer and recurrent neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    15. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    16. Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).
    17. liu, Qian & li, Yulin & jiang, Hang & chen, Yilin & zhang, Jiang, 2024. "Short-term photovoltaic power forecasting based on multiple mode decomposition and parallel bidirectional long short term combined with convolutional neural networks," Energy, Elsevier, vol. 286(C).
    18. Blaifi, Sid-ali & Mellit, Adel & Taghezouit, Bilal & Moulahoum, Samir & Hafdaoui, Hichem, 2025. "A simple non-parametric model for photovoltaic output power prediction," Renewable Energy, Elsevier, vol. 240(C).
    19. Zhu, Jiebei & Li, Mingrui & Luo, Lin & Zhang, Bidan & Cui, Mingjian & Yu, Lujie, 2023. "Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction," Renewable Energy, Elsevier, vol. 208(C), pages 141-151.
    20. Liu, Wencheng & Mao, Zhizhong, 2024. "Short-term photovoltaic power forecasting with feature extraction and attention mechanisms," Renewable Energy, Elsevier, vol. 226(C).
    21. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
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