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Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Author

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  • N. Yogambal Jayalakshmi

    (Department of Electrical and Electronics Engineering, Dr. Mahalingam College of Engineering and Technology, Coimbatore 642003, India)

  • R. Shankar

    (Department of Electronics and Communication Engineering, Teegala Krishna Reddy Engineering College, Hyderabad 500097, India)

  • Umashankar Subramaniam

    (Department of Communications and Networks, Renewable Energy Lab, College of Engineering, Prince Sultan University Riyadh, Riyadh 12435, Saudi Arabia)

  • I. Baranilingesan

    (Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Arasur Coimbatore 641047, India)

  • Alagar Karthick

    (Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Arasur Coimbatore 641047, India)

  • Balasubramaniam Stalin

    (Department of Mechanical Engineering, Regional Campus Madurai, Anna University, Madurai 625019, India)

  • Robbi Rahim

    (Department of Informatics Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Sumatera Utara 20219, Indonesia)

  • Aritra Ghosh

    (College of Engineering, Mathematics and Physical Sciences, Renewable Energy, University of Exeter, Cornwall TR10 9FE, UK)

Abstract

Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.

Suggested Citation

  • N. Yogambal Jayalakshmi & R. Shankar & Umashankar Subramaniam & I. Baranilingesan & Alagar Karthick & Balasubramaniam Stalin & Robbi Rahim & Aritra Ghosh, 2021. "Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting," Energies, MDPI, vol. 14(9), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2404-:d:541995
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    References listed on IDEAS

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    Cited by:

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    2. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
    3. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
    4. 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.
    5. Abdel-Rahman Hedar & Majid Almaraashi & Alaa E. Abdel-Hakim & Mahmoud Abdulrahim, 2021. "Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces," Energies, MDPI, vol. 14(23), pages 1-29, November.
    6. Aritra Ghosh, 2022. "Recent Advances in Renewable Energy and Clean Energy," Energies, MDPI, vol. 15(9), pages 1-2, April.
    7. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).

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