IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i12p5240-d1418575.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/12/5240/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/12/5240/
    Download Restriction: no
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Chen, Fuhao & Yan, Jie & Liu, Yongqian & Yan, Yamin & Tjernberg, Lina Bertling, 2024. "A novel meta-learning approach for few-shot short-term wind power forecasting," Applied Energy, Elsevier, vol. 362(C).
    3. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
    4. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    5. Jiahui Wang & Mingsheng Jia & Shishi Li & Kang Chen & Cheng Zhang & Xiuyu Song & Qianxi Zhang, 2024. "Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
    6. Ming Cheng & Qiang Zhang & Yue Cao, 2024. "An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model," Energies, MDPI, vol. 17(15), pages 1-16, July.
    7. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Yuan, Caichenran & Cang, Junjie & Zhang, Kai & Pecht, Michael, 2024. "Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model," Energy, Elsevier, vol. 298(C).
    8. Lakhdar Nadjib Boucetta & Youssouf Amrane & Aissa Chouder & Saliha Arezki & Sofiane Kichou, 2024. "Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model," Energies, MDPI, vol. 17(7), pages 1-22, April.
    9. Fei Zhang & Xiaoying Ren & Yongqian Liu, 2024. "A Refined Wind Power Forecasting Method with High Temporal Resolution Based on Light Convolutional Neural Network Architecture," Energies, MDPI, vol. 17(5), pages 1-25, March.
    10. Meng, Anbo & Zhang, Haitao & Dai, Zhongfu & Xian, Zikang & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhu, Jianbin & Li, Hanhong & Yin, Yiding & Liu, Jiawei & Tang, Yanshu & Zhang, Bin & Yin, Hao, 2024. "An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division," Energy, Elsevier, vol. 299(C).
    11. Osman Akbulut & Muhammed Cavus & Mehmet Cengiz & Adib Allahham & Damian Giaouris & Matthew Forshaw, 2024. "Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques," Energies, MDPI, vol. 17(10), pages 1-23, May.
    12. Jinhwa Jeong & Dongkyu Lee & Young Tae Chae, 2023. "A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service," Energies, MDPI, vol. 16(22), pages 1-21, November.
    13. Ameni Boumaiza, 2024. "A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade," Energies, MDPI, vol. 17(4), pages 1-18, February.
    14. Xin Ren & Yimei Wang & Zhi Cao & Fuhao Chen & Yujia Li & Jie Yan, 2023. "Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting," Energies, MDPI, vol. 16(17), pages 1-13, August.
    15. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
    16. Zhang, Yue & Wang, Yeqin & Zhang, Chu & Qiao, Xiujie & Ge, Yida & Li, Xi & Peng, Tian & Nazir, Muhammad Shahzad, 2024. "State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural netw," Applied Energy, Elsevier, vol. 356(C).
    17. Xiang, Ling & Fu, Xiaomengting & Yao, Qingtao & Zhu, Guopeng & Hu, Aijun, 2024. "A novel model for ultra-short term wind power prediction based on Vision Transformer," Energy, Elsevier, vol. 294(C).
    18. Yang, Han & Yuan, Weimin & Zhu, Weijun & Sun, Zhenye & Zhang, Yanru & Zhou, Yingjie, 2024. "Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology," Applied Energy, Elsevier, vol. 364(C).
    19. Jinhua Zhang & Hui Li & Peng Cheng & Jie Yan, 2024. "Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network," Energies, MDPI, vol. 17(2), pages 1-16, January.
    20. Yuhan Wu & Chun Xiang & Heng Qian & Peijian Zhou, 2024. "Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm," Energies, MDPI, vol. 17(17), pages 1-21, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5240-:d:1418575. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.