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

Energies

LeeDeog Hyun¹  Anil Bhujel¹  JayantaKumar Basak¹  FrankGyan Okyere¹  Elanchezhian Arulmozhi²  HyeonTae Kim²  Jihoon Park²  Fawad Khan²  Mustafa Jaihuni² 

[1] Life Science), Jinju 52828, Korea;;Department of Bio-Systems Engineering, Gyeongsang National University (Institute of Agriculture &

关键词: solar irradiance;    bi-gru;    arima;    algorithm;    naive decomposition;    short and very short terms;    computational efficiency;   

DOI  :  10.3390/en13020435

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown