A base station microgrid traffic prediction method based on IOOA-CNN-BiLSTM DOI Open Access
Ming Yan, Wenhao Guo, S. Xian

и другие.

Journal of Physics Conference Series, Год журнала: 2025, Номер 3000(1), С. 012029 - 012029

Опубликована: Апрель 1, 2025

Abstract The rapid advancement of 5G technology has raised significant concerns regarding the energy consumption base stations for mobile network operators. Integrating traditional station power supply systems with microgrids to maximize utilization renewable demonstrated considerable potential in addressing challenges faced by stations. However, inherent randomness communication traffic loads adversely affects reliable operation microgrids. To tackle this issue, we propose a prediction model based on deep learning methods. Initially, reference scenario microgrid is established, followed employment an Improved Osprey Optimization Algorithm (IOOA) enhance convergence speed and mitigate risk local optima. Ultimately, key parameters CNN-BiLSTM are optimized using IOOA. Experimental results from real datasets corroborate superiority proposed concerning MAPE R 2 indicators, as well perform effectively savings.

Язык: Английский

Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model DOI Creative Commons
Akshat Gaurav, Brij B. Gupta, Razaz Waheeb Attar

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 19, 2025

Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, work suggests a new method to find drivers by means Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection salp swarm (SSO) hyperparameter tuning based driving behavior. The proposed achieves an accuracy 92%, precision 91%, recall 93%, F1-score significantly outperforming traditional machine learning models such as XGBoost, CatBoost, Support Vector Machines. These findings show how strong successful our improved is precisely spotting drivers, thereby providing useful instrument safe quick systems.

Язык: Английский

Процитировано

0
Osprey Algorithm-Based Optimization of Selective Laser Melting Parameters for Enhanced Hardness and Wear Resistance in AlSi10Mg Alloy DOI Creative Commons

Nagareddy Gadlegaonkar,

Premendra J. Bansod,

Avinash Lakshmikanthan

и другие.

Journal of Materials Research and Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A base station microgrid traffic prediction method based on IOOA-CNN-BiLSTM DOI Open Access
Ming Yan, Wenhao Guo, S. Xian

и другие.

Journal of Physics Conference Series, Год журнала: 2025, Номер 3000(1), С. 012029 - 012029

Опубликована: Апрель 1, 2025

Abstract The rapid advancement of 5G technology has raised significant concerns regarding the energy consumption base stations for mobile network operators. Integrating traditional station power supply systems with microgrids to maximize utilization renewable demonstrated considerable potential in addressing challenges faced by stations. However, inherent randomness communication traffic loads adversely affects reliable operation microgrids. To tackle this issue, we propose a prediction model based on deep learning methods. Initially, reference scenario microgrid is established, followed employment an Improved Osprey Optimization Algorithm (IOOA) enhance convergence speed and mitigate risk local optima. Ultimately, key parameters CNN-BiLSTM are optimized using IOOA. Experimental results from real datasets corroborate superiority proposed concerning MAPE R 2 indicators, as well perform effectively savings.

Язык: Английский

Процитировано

0