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Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

Author

Listed:
  • Neethu Elizabeth Michael

    (Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai P.O. Box 345055, United Arab Emirates)

  • Manohar Mishra

    (Department of Electrical and Electronics Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar P.O. Box 751030, India)

  • Shazia Hasan

    (Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai P.O. Box 345055, United Arab Emirates)

  • Ahmed Al-Durra

    (Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

Abstract

Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m 2 ) and the Plane Of Array (POA) irradiance (unit: W/m 2 ) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error ( RMSE ), Mean Absolute Percentage Error ( MAPE ), Mean Squared Error ( MAE ), and R 2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R 2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R 2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.

Suggested Citation

  • Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2150-:d:771767
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    References listed on IDEAS

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    Cited by:

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    2. Hammond, Joshua E. & Lara Orozco, Ricardo A. & Baldea, Michael & Korgel, Brian A., 2024. "Short-term solar irradiance forecasting under data transmission constraints," Renewable Energy, Elsevier, vol. 233(C).
    3. Konstantinos Mira & Francesca Bugiotti & Tatiana Morosuk, 2023. "Artificial Intelligence and Machine Learning in Energy Conversion and Management," Energies, MDPI, vol. 16(23), pages 1-36, November.
    4. Jincun Liu & Kangji Li & Wenping Xue, 2024. "Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network," Energies, MDPI, vol. 17(7), pages 1-21, March.
    5. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.

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