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Deep-Learning-Based Adaptive Model for Solar Forecasting Using Clustering

Author

Listed:
  • Sourav Malakar

    (A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata 700073, India
    These authors contributed equally to this work.)

  • Saptarsi Goswami

    (Bangabasi Morning College, University of Calcutta, Kolkata 700073, India
    These authors contributed equally to this work.)

  • Bhaswati Ganguli

    (Department of Statistics, University of Calcutta, Kolkata 700073, India)

  • Amlan Chakrabarti

    (A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata 700073, India)

  • Sugata Sen Roy

    (Department of Statistics, University of Calcutta, Kolkata 700073, India)

  • K. Boopathi

    (National Institute of Wind Energy (NIWE), The Ministry of New and Renewable Energy, Government of India, New Delhi 110003, India)

  • A. G. Rangaraj

    (National Institute of Wind Energy (NIWE), The Ministry of New and Renewable Energy, Government of India, New Delhi 110003, India)

Abstract

Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in suboptimal performance. In the proposed model, different categories of cloud movement are discovered using K-medoid clustering. To ensure broader variation in cloud movements, neighboring stations were also used that were selected using a dynamic time warping (DTW)-based similarity score. Next, cluster-specific models were constructed. At the prediction time, the current weather condition is first matched with the different weather groups found through clustering, and a cluster-specific model is subsequently chosen. As a result, multiple models are dynamically used for a particular day and solar station, which improves performance over a single site-specific model. The proposed model achieved 19.74% and 59% less normalized root mean square error (NRMSE) and mean rank compared to the benchmarks, respectively, and was validated for nine solar stations across two regions and three climatic zones of India.

Suggested Citation

  • Sourav Malakar & Saptarsi Goswami & Bhaswati Ganguli & Amlan Chakrabarti & Sugata Sen Roy & K. Boopathi & A. G. Rangaraj, 2022. "Deep-Learning-Based Adaptive Model for Solar Forecasting Using Clustering," Energies, MDPI, vol. 15(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3568-:d:814752
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    References listed on IDEAS

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