A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms

Mustafa Jaihuni, Jayanta Kumar Basak, Fawad Khan, Frank Gyan Okyere, Elanchezhian Arulmozhi, Anil Bhujel, Jihoon Park, Lee Deog Hyun and Hyeon Tae Kim
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Mustafa Jaihuni: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Jayanta Kumar Basak: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Fawad Khan: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Frank Gyan Okyere: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Elanchezhian Arulmozhi: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Anil Bhujel: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Jihoon Park: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Lee Deog Hyun: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea
Hyeon Tae Kim: Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture & Life Science), Jinju 52828, Korea

Energies, 2020, vol. 13, issue 2, 1-20

Abstract: Solar renewable energy (SRE) applications are substantial in eradicating the rising global energy shortages and reversing the approaching environmental apocalypse. Hence, effective solar irradiance forecasting models are crucial in utilizing SRE efficiently. This paper introduces a partially amended hybrid model (PAHM) by the implementation of a new algorithm. The algorithm innovatively utilizes bi-directional gated unit (Bi-GRU), autoregressive integrated moving average (ARIMA) and naive decomposition models to predict solar irradiance in 5-min and 60-min intervals. Meanwhile, the models’ generalizability strengths would be tested under an 11-fold cross-validation and are further classified according to their computational costs. The dataset consists of 32 months’ solar irradiance and weather conditions records. A fundamental result of this study was that the single models (Bi-GRU and ARIMA) outperformed the hybrid models (PAHM, classical hybrid model) in the 5-min predictions, negating the assumptions that hybrid models oust single models in every time interval. PAHM provided the highest accuracy level in the 60-min predictions and improved the accuracy levels of the classical hybrid model by 5%, on average. The single models were rigorous under the 11-fold cross-validation, performing well with different datasets; although the computational efficiency of the Bi-GRU model was, by far, the best among the models.

Keywords: solar irradiance; bi-GRU; ARIMA; algorithm; naive decomposition; short and very short terms; computational efficiency (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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