Sustainable Computing-Informatics & Systems最新文献_第2页

Estimating energy consumption of neural networks with joint Structure–Device encoding

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101062

Chaopeng Guo, Shiyu Wang, Ruolan Xie, Jie Song

{"title":"Estimating energy consumption of neural networks with joint Structure–Device encoding","authors":"Chaopeng Guo, Shiyu Wang, Ruolan Xie, Jie Song","doi":"10.1016/j.suscom.2024.101062","DOIUrl":"10.1016/j.suscom.2024.101062","url":null,"abstract":"<div><div>The surge in IoT devices has led to an increase in energy consumption, necessitating the optimization of neural networks deployed on these energy-constrained devices to reduce power usage. Although various techniques, such as pruning and quantization, can reduce the size and computational requirements of neural networks, the resulting energy savings still need to be verified through resource-intensive inference processes, which require cumbersome adjustments to measurement devices and neural network deployment. To address these challenges, we propose SDEnergy, a novel approach that combines Structure–Device encoding to quickly and accurately predict the Energy consumption of neural networks across various devices. SDEnergy utilizes graph neural networks to extract structural features of neural networks and employs fully connected networks to extract device features, using their fusion for energy consumption prediction. Experimental validation demonstrates that SDEnergy has established state-of-the-art results on our dataset based on NAS-Bench-101 and various IoT device parameter scenarios, with a mean absolute percentage error of 5.35%.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101062"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of nonparametric ML on forecasting the production of a large-scale solar power plant: Kom-Ombo case study

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101074

M. Hammad , Sarah Khalil , I.M. Mahmoud

{"title":"Application of nonparametric ML on forecasting the production of a large-scale solar power plant: Kom-Ombo case study","authors":"M. Hammad , Sarah Khalil , I.M. Mahmoud","doi":"10.1016/j.suscom.2024.101074","DOIUrl":"10.1016/j.suscom.2024.101074","url":null,"abstract":"<div><div>Renewable Energy grew by 50 % globally in 2023, where three quarters of this energy is coming from solar PVs. The variable production of solar energy needed for PVs makes the prediction of the output power vital for avoiding technological and economic issues. Machine Learning (ML) models were used as a nonparametric approach to predict power output in several studies but only on small-scale solar power plants. This work investigates the implementation of several ML algorithms to predict the PV output power of large-scale solar power plants, where the Kom Ombo 26 MW power plant is taken as a case study. The Liner Regression (LR), Decision Tree (DT), and Random Forest (RF) algorithms were tested, where the LR model showed the lowest RMSE and R<sup>2</sup> values and was further improved after removing the night hours from the dataset. In addition, the Long Short-Term Memory (LSTM) model showed the highest accuracy when used with the historical records of the Kom Ombo power plant. Finally, the LSTM model was used to predict the PV output power for the Kom Ombo power plant to choose the maintenance day of the plant which resulted in substantial power and profit savings. Similarly to [6] who used Quantile Regression Forests (QRF) on a 2 MW solar power plant, we were able to show that nonparametric ML can be reliable in forecasting power output from a 20 MW solar power plant.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101074"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving energy efficiency in WSN through adaptive memetic-based clustering and routing for resource management

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101073

Vimalarani C , CP Thamil Selvi , B. Gopinathan , T. Kalavani

{"title":"Improving energy efficiency in WSN through adaptive memetic-based clustering and routing for resource management","authors":"Vimalarani C , CP Thamil Selvi , B. Gopinathan , T. Kalavani","doi":"10.1016/j.suscom.2024.101073","DOIUrl":"10.1016/j.suscom.2024.101073","url":null,"abstract":"<div><div>Efficient resource allocation in Wireless Sensor Networks (WSNs) is essential due to the constrained energy resources of sensor nodes and complex network dynamics. Existing clustering and routing methods often fail to optimize energy usage and ensure network stability under varying conditions. This research article introduces the Hybrid Memetic Evolutionary Algorithm (HMEA), which combines adaptive memetic-based clustering and evolutionary optimization to address energy-efficient clustering and routing. The HMEA dynamically selects cluster heads and optimizes transmission paths considering node energy levels and network topology, minimizing energy consumption and extending network lifetime. Simulation results demonstrate that the HMEA outperforms conventional methods, including Particle Swarm Optimization and Genetic Algorithm, in terms of energy efficiency, network throughput, and packet delivery ratio, particularly in large-scale networks. This approach advances robust resource allocation mechanisms for sustainable WSN operations.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101073"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-level decision tree-based smart electricity analysis framework for sustainable city

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101069

Tariq Ahamed Ahanger , Munish Bhatia , Abdullah Albanyan , Abdulrahman Alabduljabbar

{"title":"Bi-level decision tree-based smart electricity analysis framework for sustainable city","authors":"Tariq Ahamed Ahanger , Munish Bhatia , Abdullah Albanyan , Abdulrahman Alabduljabbar","doi":"10.1016/j.suscom.2024.101069","DOIUrl":"10.1016/j.suscom.2024.101069","url":null,"abstract":"<div><div>The revolutionizing influence of the Internet of Things (IoT) paradigm has greatly enhanced the service-delivery aspects of electricity consumption, allowing for smart energy distribution and trustworthy electric appliances. The current research presents a novel technique for detecting electricity usage in smart homes using IoT technology. Poor electricity distribution has greatly impacted daily life along with inefficient power resource allocation. This research assesses the spatial–temporal efficiency with which power grid operators distribute electrical energy resources. The efficient distribution of energy resources is achieved by calculating the spatial–temporal utilization measure for each residence of a geographical region. Also, to help power grid managers optimize the spatial–temporal allocation of energy resources, a two-level threshold-based decision-tree model is presented. For performance assessment, four smart homes are tracked for 2 months in a simulated environment. Statistical results acquired for Delay (119.61s), Reliability (82.23%), Stability (71.12%), Classification Effectiveness (Precision (95.56%), Sensitivity (95.96%), and Specificity (95.25%)), and Decision-making Efficiency (92.21%) show that the presented approach significantly outperforms state-of-the-art data analysis techniques.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101069"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing task offloading with metaheuristic algorithms across cloud, fog, and edge computing networks: A comprehensive survey and state-of-the-art schemes

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101080

Amir Masoud Rahmani , Amir Haider , Parisa Khoshvaght , Farhad Soleimanian Gharehchopogh , Komeil Moghaddasi , Shakiba Rajabi , Mehdi Hosseinzadeh

{"title":"Optimizing task offloading with metaheuristic algorithms across cloud, fog, and edge computing networks: A comprehensive survey and state-of-the-art schemes","authors":"Amir Masoud Rahmani , Amir Haider , Parisa Khoshvaght , Farhad Soleimanian Gharehchopogh , Komeil Moghaddasi , Shakiba Rajabi , Mehdi Hosseinzadeh","doi":"10.1016/j.suscom.2024.101080","DOIUrl":"10.1016/j.suscom.2024.101080","url":null,"abstract":"<div><div>The Internet of Things (IoT) significantly impacts various industries, enabling better connectivity and real-time data exchange for applications ranging from smart cities to healthcare. Integrating cloud, fog, and edge computing is essential for managing increased data and processing needs as IoT networks become complex. Cloud computing provides extensive storage and powerful computing capabilities but can experience delays due to the distance data must travel. Fog computing addresses these delays by processing data closer to its source, while edge computing reduces them even further by processing data directly on IoT devices. Effective management of these computing layers requires strategic task offloading, which involves moving tasks to the most appropriate computing layer to balance latency, energy consumption, and operational efficiency. Several strategies have been developed to optimize network communication and task offloading, with metaheuristic algorithms emerging as promising approaches. Inspired by natural processes, these algorithms are skilled at searching complex spaces to find near-optimal solutions for efficient and dynamic task offloading. This review provides a detailed analysis of how metaheuristic algorithms optimize task offloading. It evaluates their effectiveness in improving system performance, managing resources, and reducing costs. The review also identifies the current challenges in this area and suggests future research directions to advance this field.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101080"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustainable supply chain management: A green computing approach using deep Q-networks

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101063

Di Yuan, Yue Wang

{"title":"Sustainable supply chain management: A green computing approach using deep Q-networks","authors":"Di Yuan, Yue Wang","doi":"10.1016/j.suscom.2024.101063","DOIUrl":"10.1016/j.suscom.2024.101063","url":null,"abstract":"<div><div>This paper addresses the challenges of resource allocation and inventory management in supply chain systems by constructing an intelligent supply chain optimization model based on Deep Q-Networks (ISCO-DQ), emphasizing eco-efficiency. Initially, the study builds a supply chain model that incorporates supplier-customer relationships, guided by the principles of green computing to minimize environmental impact. The model applies Markov Decision Processes to develop a framework for sustainable supplier inventory control, focusing on reducing waste and optimizing resource usage. Utilizing the function approximation capabilities of Deep Q-Networks, the model not only achieves intelligent resource allocation but also prioritizes energy-efficient practices in inventory management. Experimental results indicate that the ISCO-DQ inventory control model converges to approximately −41,400 and −181,300 after around 100 and 300 cycles, respectively, under customer demand distributions that follow normal distributions. Furthermore, compared to traditional single-period stochastic and fixed-order quantity inventory control models, the total cost of the ISCO-DQ model is reduced by an average of 6.7 % and 16 %, respectively, while minimizing carbon emissions associated with overproduction and excess inventory. Additionally, the ISCO-DQ model significantly mitigates costs arising from demand uncertainty by quickly adapting to fluctuations and optimizing inventory strategies, thereby fostering a circular economy. This demonstrates that the ISCO-DQ inventory control model effectively addresses inefficiencies, inflexibility, and suboptimal resource allocation in conventional supply chain management, ultimately promoting sustainable development and environmental stewardship for enterprises.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101063"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-stage optimal pre-scheduling strategy for power system inertia assessment and replenishment under extreme weather events

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101079

Yang Wang , Pai Pang , Buyang Qi , Xianan Wang , Zhenghui Zhao

{"title":"A two-stage optimal pre-scheduling strategy for power system inertia assessment and replenishment under extreme weather events","authors":"Yang Wang , Pai Pang , Buyang Qi , Xianan Wang , Zhenghui Zhao","doi":"10.1016/j.suscom.2024.101079","DOIUrl":"10.1016/j.suscom.2024.101079","url":null,"abstract":"<div><div>This paper addresses the challenges posed by reduced power system inertia due to the large-scale renewable energy integration and the threats from frequent extreme weather events. It proposes a strategy to enhance power system resilience by incorporating inertia participation during such events. The strategy derives critical inertia demand formulas based on two key factors under extreme weather, establishing a linearized inertia assessment model. Additionally, considering the vulnerability of power lines to extreme weather events, we propose the Resilience Reserve Factor (RRF). It employs three resilience evaluation indexes to delineate the system's demand for inertia supply, efficiently targeting vulnerable areas for inertia reinforcement, thereby comprehensively enhancing the resilience of the power grid. Lastly, based on the critical inertia demand constraint criterion, we establish a two-stage pre-scheduling strategy incorporating both day-ahead planning and real-time correction while considering assessment accuracy. This approach transforms the inertia assessment problem into a resilience optimization problem, yielding the scheduling status of each generator unit and inertia replenishment results during extreme weather after iteration. The optimized strategy is validated through simulations on the improved IEEE39 buses system. Furthermore, this study employs a frequency response model to investigate the spatial distribution characteristics of inertia. The results indicate that this optimization strategy enables efficient scheduling of resources before and after extreme weather events. In addition to improving the economic performance of the power system, it significantly enhances system resilience by reinforcing both global and localized support during critical disaster resistance phases.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101079"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Markov chain-based dynamic scheduling to improve load balancing performance in fog-cloud environment

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101077

Navid Khaledian , Shiva Razzaghzadeh , Zeynab Haghbayan , Marcus Völp

{"title":"Hybrid Markov chain-based dynamic scheduling to improve load balancing performance in fog-cloud environment","authors":"Navid Khaledian , Shiva Razzaghzadeh , Zeynab Haghbayan , Marcus Völp","doi":"10.1016/j.suscom.2024.101077","DOIUrl":"10.1016/j.suscom.2024.101077","url":null,"abstract":"<div><div>Fog computing is a distributed computing paradigm that has become essential for driving Internet of Things (IoT) applications due to its ability to meet the low latency requirements of increasing IoT applications. However, fog servers can become overburdened as many IoT applications need to run on these resources, potentially leading to decreased responsiveness. Additionally, the need to handle real-world challenges such as load instability, makespan, and underutilization of virtual machine (VM) devices has driven an exponential increase in demand for effective task scheduling in IoT-based fog and cloud computing environments. Therefore, scheduling IoT applications in heterogeneous fog computing systems effectively and flexibly is crucial. The limited processing resources of fog servers make the application of ideal but computationally costly procedures more challenging. To address these difficulties, we propose using an Arithmetic Optimization Algorithm (AOA) for task scheduling and a Markov chain to forecast the load of VMs as fog and cloud layer resources. This approach aims to establish an environmentally load-balanced framework that reduces energy usage and delay. The simulation results indicate that the proposed method can improve the average makespan, delay, and Performance Improvement Rate (PIR) by 8.29 %, 11.72 %, and 4.66 %, respectively, compared to the crow, firefly, and grey wolf algorithms (GWA).</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101077"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of distributed energy resources along with Network Reconfiguration for cost-benefit analysis

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2024.101078

G. Manikanta , Ashish Mani , Anjali Jain , Ramya Kuppusamy , Yuvaraja Teekaraman

{"title":"Implementation of distributed energy resources along with Network Reconfiguration for cost-benefit analysis","authors":"G. Manikanta , Ashish Mani , Anjali Jain , Ramya Kuppusamy , Yuvaraja Teekaraman","doi":"10.1016/j.suscom.2024.101078","DOIUrl":"10.1016/j.suscom.2024.101078","url":null,"abstract":"<div><div>Increased demand of power in distribution networks (DN) driven by various sectors like industrial, commercial, municipal, residential, and irrigation necessitates alternative solutions such as Distributed Generators (DGs), capacitors, and Network Reconfiguration (NR). Addressing this challenge involves optimizing the opening of tie line switches and determining the optimal placement and capacity of capacitors and DGs, which poses a complex optimization problem involving both discrete and continuous variables. To tackle this, an Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA), combining principles from Quantum computing and Evolutionary Algorithms, is employed. This study emphasizes holistic benefits, specifically aiming to maximize economic gains in the distribution system with the installation of DGs, capacitors, and NR along with minimization of power losses. In this paper, two cases are explored. In the first case, seven scenarios’ analyses system losses with load variations, each scenario running twenty-five independent iterations. Performance metrics has been computed to reveal that simultaneous implementation of NR, DGs, and capacitors significantly reduces power losses compared to independent implementations. The second case introduces an additional objective of maximizing economic benefits. This involves considering factors like DG and capacitor location, capacity, line losses, and various costs such as operational, maintenance, and installation costs. The results tabulated in paper demonstrate that operating DGs in parallel with capacitors and NR not only minimizes power losses but also maximizes distribution utilities' profits.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101078"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bio-inspired optimizer with deep learning model for energy management system in electric vehicles

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-01 DOI: 10.1016/j.suscom.2025.101082

C. Srinivasan , C. Sheeba Joice

{"title":"Bio-inspired optimizer with deep learning model for energy management system in electric vehicles","authors":"C. Srinivasan , C. Sheeba Joice","doi":"10.1016/j.suscom.2025.101082","DOIUrl":"10.1016/j.suscom.2025.101082","url":null,"abstract":"<div><div>The rising popularity of electric vehicles (EVs) stems from their enhanced performance and environmental benefits. A critical challenge exists in optimizing the performance and extending the battery life of EVs, which depends on the accurate prediction of State of Charge (SOC) and State of Health (SOH). The Battery Management Systems (BMS) is essential for an EV’s Energy Management System (EMS). The current methodologies often fail to achieve the required precision, leading to suboptimal BMS that can compromise EV efficiency and reliability. To address these challenges, a merged SOC and SOH prediction approach is proposed. To maximize prediction accuracy, a hybrid Deep Learning (DL) model incorporating bio-inspired optimization algorithms such as Elephant Herding Optimization (EHO), Honey Badger Optimization (HBO), and Moth-Flame Optimization (MFO) is utilized. The architecture comprises two Convolutional Neural Networks (CNN) and an Autoencoder (AE), integrated with a Bidirectional Long Short-Term Memory (BLSTM) layer and a single Long Short-Term Memory (LSTM) layer for encoding and decoding tasks. The three optimized hybrid DL models were validated using standard benchmark datasets such as the Oxford Battery Aging Dataset, NASA, and CALCE. The prediction results of the merged SOC and SOH prediction from the three bio-inspired hybrid DL models were compared with those of the separate SOC prediction technique. The results of the merged SOC and SOH predictions were compared with traditional separate SOC prediction techniques, demonstrating superior performance. Notably, the HBO-Hybrid DL model achieved the highest R-squared (R2) values of 0.991 for SOC and 0.996 for SOH</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101082"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0