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A Deep Learning-Based Solar Power Generation Forecasting Method Applicable to Multiple Sites

Author

Listed:
  • Seon Young Jang

    (Department of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10504, Gyeonggi-do, Republic of Korea)

  • Byung Tae Oh

    (Department of Computer Engineering, Korea Aerospace University, Goyang-si 10504, Gyeonggi-do, Republic of Korea)

  • Eunsung Oh

    (Department of Electrical Engineering, College of IT Convergence, Global Campus, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of Korea)

Abstract

This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model’s adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources.

Suggested Citation

  • Seon Young Jang & Byung Tae Oh & Eunsung Oh, 2024. "A Deep Learning-Based Solar Power Generation Forecasting Method Applicable to Multiple Sites," Sustainability, MDPI, vol. 16(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5240-:d:1418575
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    References listed on IDEAS

    as
    1. Elham M. Al-Ali & Yassine Hajji & Yahia Said & Manel Hleili & Amal M. Alanzi & Ali H. Laatar & Mohamed Atri, 2023. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    2. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    3. 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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