Leveraging advanced AI algorithms with transformer-infused recurrent neural networks to optimize solar irradiance forecasting Article Swipe
Muhammad Sabir Naveed
,
Muhammad Fainan Hanif
,
Mohamed Metwaly
,
Imran Iqbal
,
Ehtisham Lodhi
,
Dage Liu
,
Jianchun Mi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2024.1485690
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2024.1485690
Solar energy (SE) is vital for renewable energy generation, but its natural fluctuations present difficulties in maintaining grid stability and planning. Accurate forecasting of solar irradiance (SI) is essential to address these challenges. The current research presents an innovative forecasting approach named as Transformer-Infused Recurrent Neural Network (TIR) model. This model integrates a Bi-Directional Long Short-Term Memory (BiLSTM) network for encoding and a Gated Recurrent Unit (GRU) network for decoding, incorporating attention mechanisms and positional encoding. This model is proposed to enhance SI forecasting accuracy by effectively utilizing meteorological weather data, handling overfitting, and managing data outliers and data complexity. To evaluate the model’s performance, a comprehensive comparative analysis is conducted, involving five algorithms: Artificial Neural Network (ANN), BiLSTM, GRU, hybrid BiLSTM-GRU, and Transformer models. The findings indicate that employing the TIR model leads to superior accuracy in the analyzed area, achieving R 2 value of 0.9983, RMSE of 0.0140, and MAE of 0.0092. This performance surpasses those of the alternative models studied. The integration of BiLSTM and GRU algorithms with the attention mechanism and positional encoding has been optimized to enhance the forecasting of SI. This approach mitigates computational dependencies and minimizes the error terms within the model.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fenrg.2024.1485690
- OA Status
- gold
- Cited By
- 12
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403236865
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403236865Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fenrg.2024.1485690Digital Object Identifier
- Title
-
Leveraging advanced AI algorithms with transformer-infused recurrent neural networks to optimize solar irradiance forecastingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-08Full publication date if available
- Authors
-
Muhammad Sabir Naveed, Muhammad Fainan Hanif, Mohamed Metwaly, Imran Iqbal, Ehtisham Lodhi, Dage Liu, Jianchun MiList of authors in order
- Landing page
-
https://doi.org/10.3389/fenrg.2024.1485690Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fenrg.2024.1485690Direct OA link when available
- Concepts
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Irradiance, Transformer, Solar irradiance, Artificial neural network, Algorithm, Computer science, Photovoltaic system, Environmental science, Machine learning, Artificial intelligence, Engineering, Meteorology, Electrical engineering, Voltage, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 12Per-year citation counts (last 5 years)
- References (count)
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76Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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