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Exploration of Solar Irradiance in Thar Desert Using Time Series Model

  • SOLAR RADIATION AND ITS FORECASTING
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Abstract

The present study concentrates on the exploration of solar irradiance in the Thar desert at eight selected locations, including Bhadla and Dhirubhai Ambani solar parks. For this, we first perform daily, weekly, and monthly solar irradiance prediction using five time-series models, namely AR, MA, ARMA, ARIMA, and seasonal ARIMA (SARIMA). The dataset of necessity includes hourly solar irradiance values of 21 yr (2001–2021) from NASA’s power project. As these time series models turn out to be inadequate to capture seasonality across temporal resolution, we additionally implement the window sliding ARIMA (WSARIMA) and LSTM to incorporate possible nonlinearity and seasonality in the dataset. Based on the three standard indicators, namely RMSE, MAPE, and MAE, we observe that LSTM outperforms other models at daily and weekly time resolution, whereas ARMA turns out to be the best on monthly dataset. The emanated results suggest that all locations reveal a high potential for harnessing solar power. The present analysis, therefore, enables solar irradiance exploration in the Thar desert through different time series models.

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ACKNOWLEDGMENTS

We sincerely acknowledge the constructive comments and useful suggestions of the reviewer and the editor. The first author is grateful to CSIR, New Delhi, India (Ref. no. Nov/06/2020(i)EU-V) for providing financial support in terms of the JRF. The second author would like to express her appreciation to UGC, New Delhi (Ref. no. 1026/UGC-CSIR-June 2018) for the JRF and the SRF.

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Authors and Affiliations

  1. Department of Mathematics, Birla Institute of Technology and Sciences Pilani, Pilani Campus, 333031, Jhunjhunu, Rajasthan, India

    Sakshi Shukla, Sarita Sheoran & Sumanta Pasari

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  1. Sakshi Shukla
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  2. Sarita Sheoran
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  3. Sumanta Pasari
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Correspondence to Sumanta Pasari.

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Shukla, S., Sheoran, S. & Pasari, S. Exploration of Solar Irradiance in Thar Desert Using Time Series Model. Appl. Sol. Energy 58, 876–888 (2022). https://doi.org/10.3103/S0003701X22060147

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  • DOI: https://doi.org/10.3103/S0003701X22060147

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