Evaluation and optimization of fuel economy for an SUV installed with automobile exhaust thermoelectric generators

Applied Thermal Engineering, Год журнала: 2025, Номер unknown, С. 125605 - 125605

Опубликована: Янв. 1, 2025

Язык: Английский

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Multi-optimized reconfiguration of hybrid photovoltaic-thermoelectric generation (PV-TEG) system for performance enhancement

Energy Conversion and Management, Год журнала: 2024, Номер 307, С. 118373 - 118373

Опубликована: Март 30, 2024

Язык: Английский

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Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms

Natural Resources Research, Год журнала: 2024, Номер 33(5), С. 2037 - 2062

Опубликована: Июнь 19, 2024

Язык: Английский

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Global advancements of solar thermoelectric generators application, limitations, and prospects: A comprehensive review

Applied Thermal Engineering, Год журнала: 2024, Номер 257, С. 124231 - 124231

Опубликована: Авг. 24, 2024

Язык: Английский

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Май 27, 2024

Язык: Английский

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Layer-deepened current harmonized improvement method: An optimal electrical array configuration method for performance optimization of a thermoelectric generator

Applied Thermal Engineering, Год журнала: 2024, Номер 248, С. 123143 - 123143

Опубликована: Апрель 8, 2024

Язык: Английский

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Enhancement in Optimal Resource-Based Data Transmission Over LPWAN Using a Deep Adaptive Reinforcement Learning Model Aided by Novel Remora With Lotus Effect Optimization Algorithm

IEEE Access, Год журнала: 2024, Номер 12, С. 76515 - 76531

Опубликована: Янв. 1, 2024

Wireless Sensor Networks (WSN) are adopting low-power wide area networks (LPWAN), such as long-range (LoRa) networks, to increase communication standards. LoRa has been used gather sensor data for many applications, environmental monitoring. The existing system faces degradation in network performance because of interference and congestion with the development Internet-of-Things (IoT) devices. More than device parameters algorithms must be improved large IoT applications. In massive systems, resource allocation is effectively performed using new reinforcement learning machine approaches. These approaches have proven quite effective. Hence, this work implements an efficient optimal scheme effective transmission over minor power requirement aid Deep Adaptive Reinforcement Learning (DARL). required minimize while transmitting estimated help DARL model. variables optimally selected by a optimization algorithm named Integrated Remora Lotus Effect Optimization Algorithm (IR-LEOA) that executed combining (ROA) (LEA). parameters, power, channel, spreading factor, tuned same IR-LEOA. server matched agents generated Then, given network's terminal hub after generated. Throughput, energy efficiency, latency, rate analyzed strategy. effectiveness model proved conducting extensive experimentation.

Язык: Английский

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Optimization of solar thermoelectric systems operating under non-uniform irradiation

Energy Reports, Год журнала: 2025, Номер 13, С. 1525 - 1536

Опубликована: Янв. 18, 2025

Язык: Английский

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Reconfiguration strategy for solar array system on stratospheric airship based on cost-benefit analysis

Renewable Energy, Год журнала: 2025, Номер unknown, С. 122532 - 122532

Опубликована: Фев. 1, 2025

Язык: Английский

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Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments

Applied Sciences, Год журнала: 2025, Номер 15(5), С. 2702 - 2702

Опубликована: Март 3, 2025

The microgrid is a small-scale, independent power system that plays crucial role in the transition to carbon-neutral energy systems. Combined heat and (CHP) systems with storage reduce waste within microgrids, enhancing utilization efficiency. key challenge for integrated combined determining optimal configuration operation duration under different scenarios meet users’ electricity demands while minimizing both economic environmental costs. Thus, this paper presents bi-objective mathematical model solve scheduling problem of microgrid. Long Short-Term Memory–Parallel Multi-Objective Energy Valley Optimizer (LSTM-PMOEVO) framework incorporates load prediction using LSTM planning solved via PMOEVO. These strategies address challenges posed by unpredictable fluctuations complexity solving such Finally, public dataset was utilized experiments verify performance proposed algorithm. Comparisons discussions show optimization significantly improve PMOEVO, demonstrating marked advantages over six classical algorithms. In conclusion, PMOEVO developed performs excellently Scheduling Problem Biomass-Hybrid microgrids considering uncertainty. work presented provides new solution microgrid-scheduling future research, will be further advanced application real-world scenarios.

Язык: Английский

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