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

Sourav Malakar, Saptarsi Goswami, Bhaswati Ganguli, Amlan Chakrabarti, Sugata Sen Roy, K. Boopathi and A. G. Rangaraj
Additional contact information
Sourav Malakar: A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata 700073, India
Saptarsi Goswami: Bangabasi Morning College, University of Calcutta, Kolkata 700073, India
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

Energies, 2022, vol. 15, issue 10, 1-16

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.

Keywords: clearness index forecasting; cloud cover; clustering; DTW (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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