Leveraging advanced AI algorithms with transformer-infused recurrent neural networks to optimize solar irradiance forecasting DOI Creative Commons

Muhammad Sabir Naveed,

Muhammad Fainan Hanif, Mohamed Metwaly

и другие.

Frontiers in Energy Research, Год журнала: 2024, Номер 12

Опубликована: Окт. 8, 2024

Solar energy (SE) is vital for renewable generation, but its natural fluctuations present difficulties in maintaining grid stability and planning. Accurate forecasting of solar irradiance (SI) essential to address these challenges. The current research presents an innovative approach named as Transformer-Infused Recurrent Neural Network (TIR) model. This model integrates a Bi-Directional Long Short-Term Memory (BiLSTM) network encoding Gated Unit (GRU) decoding, incorporating attention mechanisms positional encoding. proposed enhance SI accuracy by effectively utilizing meteorological weather data, handling overfitting, managing data outliers complexity. To evaluate the model’s performance, comprehensive comparative analysis conducted, involving five algorithms: Artificial (ANN), BiLSTM, GRU, hybrid BiLSTM-GRU, Transformer models. findings indicate that employing TIR leads superior analyzed area, achieving R 2 value 0.9983, RMSE 0.0140, MAE 0.0092. performance surpasses those alternative models studied. integration BiLSTM GRU algorithms with mechanism has been optimized SI. mitigates computational dependencies minimizes error terms within

Язык: Английский

AI-Driven precision in solar forecasting: Breakthroughs in machine learning and deep learning DOI Creative Commons
Ayesha Nadeem, Muhammad Farhan Hanif,

Muhammad Sabir Naveed

и другие.

AIMS Geosciences, Год журнала: 2024, Номер 10(4), С. 684 - 734

Опубликована: Янв. 1, 2024

<p>The need for accurate solar energy forecasting is paramount as the global push towards renewable intensifies. We aimed to provide a comprehensive analysis of latest advancements in forecasting, focusing on Machine Learning (ML) and Deep (DL) techniques. The novelty this review lies its detailed examination ML DL models, highlighting their ability handle complex nonlinear patterns Solar Irradiance (SI) data. systematically explored evolution from traditional empirical, including machine learning (ML), physical approaches these advanced delved into real-world applications, discussing economic policy implications. Additionally, we covered variety image-based, statistical, ML, DL, foundation, hybrid models. Our revealed that models significantly enhance accuracy, operational efficiency, grid reliability, contributing benefits supporting sustainable policies. By addressing challenges related data quality model interpretability, underscores importance continuous innovation techniques fully realize potential. findings suggest integrating with offers most promising path forward improving forecasting.</p>

Язык: Английский

Процитировано

5
Enhancing solar radiation forecasting accuracy with a hybrid SA-Bi-LSTM-Bi-GRU model DOI

Girijapati Sharma,

Subhash Chandra,

Arvind Yadav

и другие.

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 19, 2025

Язык: Английский

Процитировано

0
Ensemble-Empirical-Mode-Decomposition (EEMD) on SWH prediction: The effect of decomposed IMFs, continuous prediction duration, and data-driven models DOI

Yuanye Guo,

Jicang Si, Yulian Wang

и другие.

Ocean Engineering, Год журнала: 2025, Номер 324, С. 120755 - 120755

Опубликована: Фев. 24, 2025

Язык: Английский

Процитировано

0
Multi-model integration for dynamic forecasting (MIDF): a framework for wind speed and direction prediction DOI Creative Commons

Molaka Maruthi,

Bubryur Kim,

Song Sujeen

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 15, 2025

Язык: Английский

Процитировано

0
Leveraging advanced AI algorithms with transformer-infused recurrent neural networks to optimize solar irradiance forecasting DOI Creative Commons

Muhammad Sabir Naveed,

Muhammad Fainan Hanif, Mohamed Metwaly

и другие.

Frontiers in Energy Research, Год журнала: 2024, Номер 12

Опубликована: Окт. 8, 2024

Solar energy (SE) is vital for renewable generation, but its natural fluctuations present difficulties in maintaining grid stability and planning. Accurate forecasting of solar irradiance (SI) essential to address these challenges. The current research presents an innovative approach named as Transformer-Infused Recurrent Neural Network (TIR) model. This model integrates a Bi-Directional Long Short-Term Memory (BiLSTM) network encoding Gated Unit (GRU) decoding, incorporating attention mechanisms positional encoding. proposed enhance SI accuracy by effectively utilizing meteorological weather data, handling overfitting, managing data outliers complexity. To evaluate the model’s performance, comprehensive comparative analysis conducted, involving five algorithms: Artificial (ANN), BiLSTM, GRU, hybrid BiLSTM-GRU, Transformer models. findings indicate that employing TIR leads superior analyzed area, achieving R 2 value 0.9983, RMSE 0.0140, MAE 0.0092. performance surpasses those alternative models studied. integration BiLSTM GRU algorithms with mechanism has been optimized SI. mitigates computational dependencies minimizes error terms within

Язык: Английский

Процитировано

1