BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

Yuan, Hao; Tang, Guoming; Guo, Deke; Wu, Kui; Shao, Xun; Yu, Keping; Wei, Wei

doi:10.1109/tgcn.2021.3136363

Cited by 15 publications

(2 citation statements)

References 40 publications

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“…In the future, EfficientNet deep model will be implemented and features will be refined through the Butterfly metaheuristic algorithm instead of the heuristic search approach [20]. Moreover, reinforcement learning and Graph CNN shall be applied and refined through feature selection algorithms for the better results [38][39][40][41][42].…”

Section: Discussionmentioning

confidence: 99%

Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection

et al. 2022

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Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance.

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Section: Discussionmentioning

confidence: 99%

Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection

et al. 2022

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“…Consequently, the subject of utilizing RE to power BSs in order to reduce the carbon footprint of networks has attracted academic attention. Yuan et al [23] proposed an energy-storage-assisted RE supply solution to power a BS, in which a deep reinforcement learning (DRL)-based regulating policy is utilized to flexibly regulate the battery's discharging/charging. On the basis of off-grid cellular BSs powered by integrated RE, Jahid et al [24] formulated a hybrid energy cooperation framework that optimally determines the quantities of RE exchanged among BSs to minimize both related costs and greenhouse gas emissions.…”

Section: Introductionmentioning

confidence: 99%

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology

Wang,

Li,

et al. 2023

Electronics

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Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the network. To address these issues, we first propose a SAGIN architecture with energy-harvesting devices, where the base station (BS) is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power an unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighboring BSs is designed to fully utilize RE to reduce carbon emissions. Secondly, on the basis of task offloading decisions, the UAV trajectory, and the RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emissions.

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Optimal capacity planning and operation of shared energy storage system for large-scale photovoltaic integrated 5G base stations

Zhang

Wang

Liao

et al. 2023

International Journal of Electrical Power & Energy Systems

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BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

Cited by 15 publications

References 40 publications

Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection

Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology

Optimal capacity planning and operation of shared energy storage system for large-scale photovoltaic integrated 5G base stations

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