Zobrazit v EDS

An efficient novel deep learning-based predicting model optimized by an improved DAOA algorithm for microgrid energy management

Uloženo v:
Podrobná bibliografie
Název: An efficient novel deep learning-based predicting model optimized by an improved DAOA algorithm for microgrid energy management
Autoři: Almousawi, Ali Q., Aklo, Nabil Jalil, Alhadrawi , Zaid
Zdroj: Future Technology ; Vol. 5 No. 1 (2026): February 2026 Issue; 303-313 ; 2832-0379
Informace o vydavateli: Future Publishing LLC
Rok vydání: 2025
Témata: Energy management, Improved dynamic arithmetic optimization, Photovoltaic, Wind turbine, Deep learning
Popis: This paper presents robust energy-demand and renewable power forecasts for the microgrid using deep learning-based forecasting and a metaheuristic-based optimization model. A Long Short-Term Memory (LSTM) is used to model the temporal nonlinear dynamics of the energy datasets. A new Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) is developed to fine-tune LSTM parameters, incorporating inertial weights, a mutation factor, and the triangle mutation operator to balance exploration and exploitation. The model's performance is verified on various datasets, including wind turbines (WT), photovoltaic (PV) systems, load demands, and day-ahead electricity pricing. This work shows that the IDAOA-LSTM model outperforms other strategies. Practically, the Root Mean Squared Error (RMSE) was 0.021 in the forecast of WT power and 0.031 in the case of PV power. The model performs well in predictions, with high coefficient of determination (R²) values (R² ≥ 0.98) throughout all tasks. These findings strengthen the applicability of the proposed method to enhance energy-saving measures while preserving the stable operation of those microgrid (MG) systems.
Druh dokumentu: article in journal/newspaper
Popis souboru: application/pdf
Jazyk: English
Relation: https://fupubco.com/futech/article/view/629/284; https://fupubco.com/futech/article/view/629
Dostupnost: https://fupubco.com/futech/article/view/629
Rights: Copyright (c) 2025 Future Technology
Přístupové číslo: edsbas.F068D103
Databáze: BASE
Popis
Abstrakt:This paper presents robust energy-demand and renewable power forecasts for the microgrid using deep learning-based forecasting and a metaheuristic-based optimization model. A Long Short-Term Memory (LSTM) is used to model the temporal nonlinear dynamics of the energy datasets. A new Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) is developed to fine-tune LSTM parameters, incorporating inertial weights, a mutation factor, and the triangle mutation operator to balance exploration and exploitation. The model's performance is verified on various datasets, including wind turbines (WT), photovoltaic (PV) systems, load demands, and day-ahead electricity pricing. This work shows that the IDAOA-LSTM model outperforms other strategies. Practically, the Root Mean Squared Error (RMSE) was 0.021 in the forecast of WT power and 0.031 in the case of PV power. The model performs well in predictions, with high coefficient of determination (R²) values (R² ≥ 0.98) throughout all tasks. These findings strengthen the applicability of the proposed method to enhance energy-saving measures while preserving the stable operation of those microgrid (MG) systems.