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Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting

Author

Listed:
  • Linh Bui Duy

    (Vietnam Academy of Science and Technology, Graduate University of Science and Technology, Hanoi 11307, Vietnam)

  • Ninh Nguyen Quang

    (Vietnam Academy of Science and Technology, Graduate University of Science and Technology, Hanoi 11307, Vietnam
    Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam)

  • Binh Doan Van

    (Vietnam Academy of Science and Technology, Graduate University of Science and Technology, Hanoi 11307, Vietnam
    Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam)

  • Eleonora Riva Sanseverino

    (Engineering Department, University of Palermo, 90128 Palermo, Italy)

  • Quynh Tran Thi Tu

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam
    Hawaii Natural Energy Institute, University of Hawaii at Manoa, Honolulu, HI 96822, USA)

  • Hang Le Thi Thuy

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam)

  • Sang Le Quang

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam)

  • Thinh Le Cong

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam)

  • Huyen Cu Thi Thanh

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam)

Abstract

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables.

Suggested Citation

  • Linh Bui Duy & Ninh Nguyen Quang & Binh Doan Van & Eleonora Riva Sanseverino & Quynh Tran Thi Tu & Hang Le Thi Thuy & Sang Le Quang & Thinh Le Cong & Huyen Cu Thi Thanh, 2024. "Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting," Energies, MDPI, vol. 17(16), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4174-:d:1461201
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    References listed on IDEAS

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    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
    3. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    4. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    5. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    6. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    7. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    8. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    9. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    10. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    11. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
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