Cloud computing task scheduling is not only the foundation for ensuring the efficient operation of the cloud platform, but also an important means of improving service quality and reducing costs. With the continuous development of cloud computing technology, the requirements for intelligent and automated task scheduling are also increasing. To address the demand for more efficient and flexible computations, an enhanced honey badger algorithm (HBA) utilizing two dimensional and three dimensional fractals is introduced. The digging phase of the honey badger's foraging strategy is improved by using the mathematical expressions of two dimensional and three dimensional fractals in rectangular and polar coordinates, which enhances the algorithm's performance while speeding up its convergence. The optimal solution HBACBKS-Z was selected by verification on the benchmark functions. The optimization problem of task scheduling in cloud computing systems is divided into large-scale task scheduling and small-scale task scheduling. Experiments were conducted in these two cases by using HBACBKS-Z and other traditional swarm intelligence optimization algorithms. It has been proved that HBACBKS-Z has significant advantages in terms of total cost, time cost, load cost and price cost, and can effectively solve the task scheduling optimization problem of cloud computing systems of various sizes.
{"title":"Task scheduling in cloud computing system by improved honey badger optimization algorithm with two dimensional and three dimensional fractals","authors":"Yu-Feng Sun, Si-Wen Zhang, Jie-Sheng Wang, Shi-Hui Zhang, Yu-Cai Wang, Xiao-Fei Sui","doi":"10.1016/j.suscom.2025.101201","DOIUrl":"10.1016/j.suscom.2025.101201","url":null,"abstract":"<div><div>Cloud computing task scheduling is not only the foundation for ensuring the efficient operation of the cloud platform, but also an important means of improving service quality and reducing costs. With the continuous development of cloud computing technology, the requirements for intelligent and automated task scheduling are also increasing. To address the demand for more efficient and flexible computations, an enhanced honey badger algorithm (HBA) utilizing two dimensional and three dimensional fractals is introduced. The digging phase of the honey badger's foraging strategy is improved by using the mathematical expressions of two dimensional and three dimensional fractals in rectangular and polar coordinates, which enhances the algorithm's performance while speeding up its convergence. The optimal solution HBACBKS-Z was selected by verification on the benchmark functions. The optimization problem of task scheduling in cloud computing systems is divided into large-scale task scheduling and small-scale task scheduling. Experiments were conducted in these two cases by using HBACBKS-Z and other traditional swarm intelligence optimization algorithms. It has been proved that HBACBKS-Z has significant advantages in terms of total cost, time cost, load cost and price cost, and can effectively solve the task scheduling optimization problem of cloud computing systems of various sizes.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101201"},"PeriodicalIF":5.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004812","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}
Pub Date : 2025-09-02DOI: 10.1016/j.suscom.2025.101199
G.K. Jabash Samuel , P. Rajendran , Papana Venkata Prasad , Chinthalacheruvu Venkata Krishna Reddy
This paper proposes a hybrid power management strategy for smart grids (SGs) that integrates renewable energy sources (RESs), such as battery energy storage systems (BESS), fuel cells (FCs), wind turbines (WT), and solar photovoltaic (PV). The GGO-AIDINN approach integrates Greylag Goose Optimization (GGO) and an Anti-Interference Dynamic Integral Neural Network (AIDINN) to address high emissions during low renewable energy (RE) availability and rising operational costs from advanced infrastructure. The GGO optimizes resource allocation and energy distribution, maximizing the use of available RE. Meanwhile, AIDINN predicts energy consumption patterns based on weather conditions, improving overall system performance. The proposed GGO-AIDINN model is implemented on MATLAB and evaluated against several existing methods, including Fuzzy Logic Control (FLC), Non-dominated Sorting Genetic Algorithm (NSGA-II), and others. Results show the hybrid method achieves significant improvements, with an operational cost of $1328 per MW, emissions of 13.76 kg per MW, and an efficiency of 98.7 %. These outcomes demonstrate that GGO-AIDINN outperforms traditional techniques, offering lower costs, reduced emissions, and enhanced system efficiency. This makes it a superior solution for sustainable power management in SGs incorporating RESs and BESS.
本文提出了一种集成可再生能源（RESs）的智能电网（SGs）混合电源管理策略，如电池储能系统（BESS）、燃料电池（fc）、风力涡轮机（WT）和太阳能光伏（PV）。GGO-AIDINN方法集成了灰雁优化（GGO）和抗干扰动态积分神经网络（AIDINN），以解决低可再生能源（RE）可用性和先进基础设施运营成本上升时的高排放问题。GGO优化资源分配和能源分配，最大限度地利用可用的可再生能源。同时，AIDINN根据天气状况预测能源消耗模式，提高整体系统性能。在MATLAB上实现了所提出的go - aidinn模型，并对几种现有方法进行了评估，包括模糊逻辑控制（FLC）、非支配排序遗传算法（NSGA-II）等。结果表明，混合方法取得了显著的改进，运行成本为1328美元/兆瓦，排放量为13.76 kg /兆瓦，效率为98.7 %。这些结果表明，go - aidinn技术优于传统技术，成本更低，排放更少，系统效率更高。这使得它成为SGs整合RESs和BESS的可持续电源管理的卓越解决方案。
{"title":"Power management for smart grids integrating renewable energy sources using Greylag goose optimization and anti-interference dynamic integral neural network","authors":"G.K. Jabash Samuel , P. Rajendran , Papana Venkata Prasad , Chinthalacheruvu Venkata Krishna Reddy","doi":"10.1016/j.suscom.2025.101199","DOIUrl":"10.1016/j.suscom.2025.101199","url":null,"abstract":"<div><div>This paper proposes a hybrid power management strategy for smart grids (SGs) that integrates renewable energy sources (RESs), such as battery energy storage systems (BESS), fuel cells (FCs), wind turbines (WT), and solar photovoltaic (PV). The GGO-AIDINN approach integrates Greylag Goose Optimization (GGO) and an Anti-Interference Dynamic Integral Neural Network (AIDINN) to address high emissions during low renewable energy (RE) availability and rising operational costs from advanced infrastructure. The GGO optimizes resource allocation and energy distribution, maximizing the use of available RE. Meanwhile, AIDINN predicts energy consumption patterns based on weather conditions, improving overall system performance. The proposed GGO-AIDINN model is implemented on MATLAB and evaluated against several existing methods, including Fuzzy Logic Control (FLC), Non-dominated Sorting Genetic Algorithm (NSGA-II), and others. Results show the hybrid method achieves significant improvements, with an operational cost of $1328 per MW, emissions of 13.76 kg per MW, and an efficiency of 98.7 %. These outcomes demonstrate that GGO-AIDINN outperforms traditional techniques, offering lower costs, reduced emissions, and enhanced system efficiency. This makes it a superior solution for sustainable power management in SGs incorporating RESs and BESS.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101199"},"PeriodicalIF":5.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048493","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}
Pub Date : 2025-08-29DOI: 10.1016/j.suscom.2025.101191
Abdennabi Morchid , Ishaq G. Muhammad Alblushi , Haris M. Khalid , Hassan Qjidaa , Rachid El Alami
Modern agriculture faces significant challenges related to water scarcity and the impacts of climate change. To ensure crop sustainability and food security, irrigation systems must be optimized. Fuzzy logic and the Internet of Things (IoT) are two cutting-edge approaches to intelligent irrigation management that adjust water delivery to plants' real needs. Conventional irrigation techniques are wasteful and ineffective. Fuzzy logic and the IoT have exciting opportunities, but integrating them presents difficulties, especially (1) concerning implementation, (2) cost, and (3) data security. In light of water shortage, food security, and sustainable development issues, this proposed article examines how IoT and fuzzy logic might be used to create smart irrigation systems. It evaluates contemporary methods for optimizing water management using fuzzy logic and the IoT, as well as the effects of climate change on irrigation. While addressing the challenges of installation costs, implementation complexity, communication reliability, and data security, the proposed review highlights the benefits of these technologies, including reduced water consumption, increased agricultural yields, automation, and environmental adaptability. The main topics of this review's final section, including the integration of new, cutting-edge technology, enhanced decision-making models, and the adoption of sustainable solutions for more resilient and effective agriculture, also address potential directions for future research. importance of the research. Due to water constraints and climate change, this study highlights the importance of intelligent irrigation systems. It showcases creative methods to maximize water management and raise agricultural productivity by fusing IoT with fuzzy logic.
{"title":"Integrating IoT and fuzzy logic for intelligent irrigation in sustainable agriculture for improving water scarcity: Benefits and challenges","authors":"Abdennabi Morchid , Ishaq G. Muhammad Alblushi , Haris M. Khalid , Hassan Qjidaa , Rachid El Alami","doi":"10.1016/j.suscom.2025.101191","DOIUrl":"10.1016/j.suscom.2025.101191","url":null,"abstract":"<div><div>Modern agriculture faces significant challenges related to water scarcity and the impacts of climate change. To ensure crop sustainability and food security, irrigation systems must be optimized. Fuzzy logic and the Internet of Things (IoT) are two cutting-edge approaches to intelligent irrigation management that adjust water delivery to plants' real needs. Conventional irrigation techniques are wasteful and ineffective. Fuzzy logic and the IoT have exciting opportunities, but integrating them presents difficulties, especially (1) concerning implementation, (2) cost, and (3) data security. In light of water shortage, food security, and sustainable development issues, this proposed article examines how IoT and fuzzy logic might be used to create smart irrigation systems. It evaluates contemporary methods for optimizing water management using fuzzy logic and the IoT, as well as the effects of climate change on irrigation. While addressing the challenges of installation costs, implementation complexity, communication reliability, and data security, the proposed review highlights the benefits of these technologies, including reduced water consumption, increased agricultural yields, automation, and environmental adaptability. The main topics of this review's final section, including the integration of new, cutting-edge technology, enhanced decision-making models, and the adoption of sustainable solutions for more resilient and effective agriculture, also address potential directions for future research. importance of the research. Due to water constraints and climate change, this study highlights the importance of intelligent irrigation systems. It showcases creative methods to maximize water management and raise agricultural productivity by fusing IoT with fuzzy logic.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101191"},"PeriodicalIF":5.7,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926276","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}
This plan presents energy scheduling in a distribution grid with multi-microgrid according to estimation of environmental, economic, flexibility, operation, and security indicators in microgrids. Microgrid has a multi-bus structure, which includes renewable solar, wind and bio-waste devices, non-renewable resources, compressed air and hydrogen storage. Study contains the three objectives optimization. The objective functions are the minimization of operation cost of microgrids and resources, the environmental pollution of microgrids and voltage deviation function. The constraints of the problem include the optimal power flow formulation of microgrids based on the flexibility and voltage security limits, the performance model of renewable/non-renewable units, and storage devices. Study has parameters of price of energy, load, and renewable phenomena as uncertainty. For their modeling, the point estimation approach is used to according to low computational time and accurately model flexibility. The ε-constraint method is used to extract the single-objective model, and fuzzy decision-making technique is used to achieve the compromise solution. This scheme has a non-convex nonlinear formulation. To access a reliable response considering low deviation for last point, a combination of red panda optimization and ant-lion optimization is used. Funding indicate the ability of plan for improve the technical, environmental, and economic conditions of microgrids. Thus, energy scheduling of the aforementioned units and storages can improve operational, economic, environmental, and voltage stability conditions of microgrids by about 59.2 %, 44.2 %, 24.5 %-75 % and 17.3 %-27.4 %, respectively. In these conditions, study achieves 100 % flexibility for microgrids. Solution approach achieves the sustainable computing conditions, such that it has the most optimal solution at low computational time and a standard deviation of 0.97 % in the final response.
{"title":"Flexibility regulation-based economic energy scheduling in multi-microgrids with renewable/non-renewable resource and stationary storage systems considering sustainable computing by hybrid metaheuristic algorithm","authors":"Ahad Faraji Naghibi , Ehsan Akbari , Saeid Shahmoradi , Mehdi Veisi , Sasan Pirouzi","doi":"10.1016/j.suscom.2025.101196","DOIUrl":"10.1016/j.suscom.2025.101196","url":null,"abstract":"<div><div>This plan presents energy scheduling in a distribution grid with multi-microgrid according to estimation of environmental, economic, flexibility, operation, and security indicators in microgrids. Microgrid has a multi-bus structure, which includes renewable solar, wind and bio-waste devices, non-renewable resources, compressed air and hydrogen storage. Study contains the three objectives optimization. The objective functions are the minimization of operation cost of microgrids and resources, the environmental pollution of microgrids and voltage deviation function. The constraints of the problem include the optimal power flow formulation of microgrids based on the flexibility and voltage security limits, the performance model of renewable/non-renewable units, and storage devices. Study has parameters of price of energy, load, and renewable phenomena as uncertainty. For their modeling, the point estimation approach is used to according to low computational time and accurately model flexibility. The ε-constraint method is used to extract the single-objective model, and fuzzy decision-making technique is used to achieve the compromise solution. This scheme has a non-convex nonlinear formulation. To access a reliable response considering low deviation for last point, a combination of red panda optimization and ant-lion optimization is used. Funding indicate the ability of plan for improve the technical, environmental, and economic conditions of microgrids. Thus, energy scheduling of the aforementioned units and storages can improve operational, economic, environmental, and voltage stability conditions of microgrids by about 59.2 %, 44.2 %, 24.5 %-75 % and 17.3 %-27.4 %, respectively. In these conditions, study achieves 100 % flexibility for microgrids. Solution approach achieves the sustainable computing conditions, such that it has the most optimal solution at low computational time and a standard deviation of 0.97 % in the final response.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101196"},"PeriodicalIF":5.7,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921922","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}
Pub Date : 2025-08-24DOI: 10.1016/j.suscom.2025.101195
Ravita Saraswat, Sathans Suhag
To ensure reliable & resilient operation of a microgrid, efficient voltage and power regulation strategies have to be in place. The instant study proposes the memetic salp swarm algorithm (MSSA) tuned fractional order proportional-integral-derivative (FOPID) control strategy towards improving operational resilience of the grid-connected microgrid, comprising solar panels, wind turbine, battery bank, and AC load, in the backdrop of solar, wind, and load uncertainties besides the eventuality of grid isolation. MATLAB® simulation results, both qualitative and quantitative, ideate effectiveness of recommended control strategy whose novelty lies in synergetic use of MSSA and FOPID, with the tuning competency of MSSA established against grey wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms.
{"title":"Memetic salp swarm algorithm optimized control for operational resilience in grid-tied microgrid","authors":"Ravita Saraswat, Sathans Suhag","doi":"10.1016/j.suscom.2025.101195","DOIUrl":"10.1016/j.suscom.2025.101195","url":null,"abstract":"<div><div>To ensure reliable & resilient operation of a microgrid, efficient voltage and power regulation strategies have to be in place. The instant study proposes the memetic salp swarm algorithm (MSSA) tuned fractional order proportional-integral-derivative (FOPID) control strategy towards improving operational resilience of the grid-connected microgrid, comprising solar panels, wind turbine, battery bank, and AC load, in the backdrop of solar, wind, and load uncertainties besides the eventuality of grid isolation. MATLAB® simulation results, both qualitative and quantitative, ideate effectiveness of recommended control strategy whose novelty lies in synergetic use of MSSA and FOPID, with the tuning competency of MSSA established against grey wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101195"},"PeriodicalIF":5.7,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903445","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}
Pub Date : 2025-08-20DOI: 10.1016/j.suscom.2025.101183
Ling Liu, Jianbo Xu, Junwen Fang, Guoli Sun
In the current fast-changing digital world, distributed networks are under severe threat in terms of security and efficiency. Their decentralized nature and expanding amount of data raise system complexity and vulnerability. At the same time, sustainable computing demands energy-efficient solutions for network operations. This research proposes a Distributed Network Security Framework Based on Blockchain within a Green Computing Framework. It introduces a Dynamic Whale Optimized Adjustable Graph Neural Network (DWO-AGNN) to assess network security. The model leverages blockchain’s decentralized and tamper-proof features, using smart contracts to enhance resilience against cyberattacks. The framework also focuses on reducing the energy footprint of security operations. Key performance metrics include security effectiveness, energy consumption, and throughput. Results show strong performance: availability at 99.0 %, integrity at 96.8 %, and confidentiality at 95.2 %. The system achieves 95.7 Megabits per Second (Mbps) throughput, reduces energy usage from 1.20 to 0.85, and cuts energy costs from $500 to $375. This research demonstrates that blockchain-based models can deliver high security while supporting environmentally responsible computing. The DWO-AGNN offers a practical solution for resilient, energy-efficient distributed networks.
{"title":"Research on optimization of distributed network security framework based on blockchain under green computing framework","authors":"Ling Liu, Jianbo Xu, Junwen Fang, Guoli Sun","doi":"10.1016/j.suscom.2025.101183","DOIUrl":"10.1016/j.suscom.2025.101183","url":null,"abstract":"<div><div>In the current fast-changing digital world, distributed networks are under severe threat in terms of security and efficiency. Their decentralized nature and expanding amount of data raise system complexity and vulnerability. At the same time, sustainable computing demands energy-efficient solutions for network operations. This research proposes a Distributed Network Security Framework Based on Blockchain within a Green Computing Framework. It introduces a Dynamic Whale Optimized Adjustable Graph Neural Network (DWO-AGNN) to assess network security. The model leverages blockchain’s decentralized and tamper-proof features, using smart contracts to enhance resilience against cyberattacks. The framework also focuses on reducing the energy footprint of security operations. Key performance metrics include security effectiveness, energy consumption, and throughput. Results show strong performance: availability at 99.0 %, integrity at 96.8 %, and confidentiality at 95.2 %. The system achieves 95.7 Megabits per Second (Mbps) throughput, reduces energy usage from 1.20 to 0.85, and cuts energy costs from $500 to $375. This research demonstrates that blockchain-based models can deliver high security while supporting environmentally responsible computing. The DWO-AGNN offers a practical solution for resilient, energy-efficient distributed networks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101183"},"PeriodicalIF":5.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893610","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}
Pub Date : 2025-08-16DOI: 10.1016/j.suscom.2025.101182
Seyed-Sajad Ahmadpour , Nima Jafari Navimipour , Muhammad Zohaib , Neeraj Kumar Misra , Mahsa Rastegar Pour , Hadi Rasmi , Sankit Kassa , Jadav Chandra Das
One such revolutionary approach to changing the nano-electronic landscape is integrating reversible logic with quantum dot technology that will replace the conventional complementary metal-oxide semiconductors (CMOS) circuits for ultra-high speed, low density, and energy-efficient digital designs. The implementation of the reversible structure under the most inflexible conditions, as executed by quantum laws, is a highly challenging task. Furthermore, the enormous occupying areas seriously compromise the accuracy of the output in quantum dot circuits. Because of this challenge, quantum circuits can be employed as fundamental building blocks in high-performance digital systems since their implementation has a key impact on overall system performance. This study discusses a paradigm shift in nanoscale digital design by using a 4 × 4 reversible gate that redefines the basis of efficiency and precision. This reversible gate is elaborately used in a reversible full-adder circuit, fully symbolizing the core of minimum area, ultra-low energy consumption, and perfect output accuracy. The proposed reversible circuits have been fully realized using quantum-dot cellular automata technology (QCA), simulated, and verified by the highly reliable tool such as Qiskit IBM and QCADesigner 2.0.3. Furthermore, simulations results demonstrated the superiority of the QCA-based proposed adder, which reduced occupied area by 7.14 %, and cell count by 11.57 %, respectively. This work resolves some problems and opens new boundaries toward the future of digital circuits by addressing the main challenges of stability and pushing the boundaries of reversible logic design.
{"title":"Scalable and low-power reversible logic for future devices: QCA and IBM-based gate realization","authors":"Seyed-Sajad Ahmadpour , Nima Jafari Navimipour , Muhammad Zohaib , Neeraj Kumar Misra , Mahsa Rastegar Pour , Hadi Rasmi , Sankit Kassa , Jadav Chandra Das","doi":"10.1016/j.suscom.2025.101182","DOIUrl":"10.1016/j.suscom.2025.101182","url":null,"abstract":"<div><div>One such revolutionary approach to changing the nano-electronic landscape is integrating reversible logic with quantum dot technology that will replace the conventional complementary metal-oxide semiconductors (CMOS) circuits for ultra-high speed, low density, and energy-efficient digital designs. The implementation of the reversible structure under the most inflexible conditions, as executed by quantum laws, is a highly challenging task. Furthermore, the enormous occupying areas seriously compromise the accuracy of the output in quantum dot circuits. Because of this challenge, quantum circuits can be employed as fundamental building blocks in high-performance digital systems since their implementation has a key impact on overall system performance. This study discusses a paradigm shift in nanoscale digital design by using a 4 × 4 reversible gate that redefines the basis of efficiency and precision. This reversible gate is elaborately used in a reversible full-adder circuit, fully symbolizing the core of minimum area, ultra-low energy consumption, and perfect output accuracy. The proposed reversible circuits have been fully realized using quantum-dot cellular automata technology (QCA), simulated, and verified by the highly reliable tool such as Qiskit IBM and QCADesigner 2.0.3. Furthermore, simulations results demonstrated the superiority of the QCA-based proposed adder, which reduced occupied area by 7.14 %, and cell count by 11.57 %, respectively. This work resolves some problems and opens new boundaries toward the future of digital circuits by addressing the main challenges of stability and pushing the boundaries of reversible logic design.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101182"},"PeriodicalIF":5.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916880","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}
Pub Date : 2025-08-14DOI: 10.1016/j.suscom.2025.101181
Xiaolin Zhang, Zhi Liu
This paper introduces a novel hybrid optimization framework for Multi-Energy Systems that jointly addresses cost efficiency, uncertainty, and demand-side flexibility. The proposed model uniquely integrates electric and thermal Load Response Plans within a unified structure and incorporates a Negative Risk Limit to explicitly control downside financial exposure under volatile conditions. A key innovation lies in the combination of scenario-based stochastic modeling and robust optimization to manage uncertainties in renewable generation, market prices, and consumer demand. The Flower Pollination Algorithm, a nature-inspired metaheuristic, is employed to efficiently solve the resulting high-dimensional problem. A residential-scale case study, involving photovoltaic panels, wind turbines, combined heat and power, boilers, electric vehicles, thermal storage, and heat pumps, demonstrates the framework’s applicability. Four simulation scenarios assess the individual and combined effects of Load Response Plans and risk constraints. Results indicate that energy purchases from upstream networks are reduced with coordinated load shifting, lowering peak hour procurement by 15–30 % compared to baseline operation. Electric vehicles exhibit active charge/discharge behavior in up to 75 % of daily time slots under joint Load Response Plan and Negative Risk Limit conditions, enhancing flexibility.
{"title":"Integrate multiple energy sources of the microgrid: Enhancing performance and sustainability in multi-energy systems","authors":"Xiaolin Zhang, Zhi Liu","doi":"10.1016/j.suscom.2025.101181","DOIUrl":"10.1016/j.suscom.2025.101181","url":null,"abstract":"<div><div>This paper introduces a novel hybrid optimization framework for Multi-Energy Systems that jointly addresses cost efficiency, uncertainty, and demand-side flexibility. The proposed model uniquely integrates electric and thermal Load Response Plans within a unified structure and incorporates a Negative Risk Limit to explicitly control downside financial exposure under volatile conditions. A key innovation lies in the combination of scenario-based stochastic modeling and robust optimization to manage uncertainties in renewable generation, market prices, and consumer demand. The Flower Pollination Algorithm, a nature-inspired metaheuristic, is employed to efficiently solve the resulting high-dimensional problem. A residential-scale case study, involving photovoltaic panels, wind turbines, combined heat and power, boilers, electric vehicles, thermal storage, and heat pumps, demonstrates the framework’s applicability. Four simulation scenarios assess the individual and combined effects of Load Response Plans and risk constraints. Results indicate that energy purchases from upstream networks are reduced with coordinated load shifting, lowering peak hour procurement by 15–30 % compared to baseline operation. Electric vehicles exhibit active charge/discharge behavior in up to 75 % of daily time slots under joint Load Response Plan and Negative Risk Limit conditions, enhancing flexibility.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101181"},"PeriodicalIF":5.7,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893612","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}
Pub Date : 2025-08-09DOI: 10.1016/j.suscom.2025.101180
Anping Wan , Shuai Peng , Khalil AL-Bukhaiti , Yunsong Ji , Shidong Ma
Offshore wind turbine gearboxes often experience malfunctions due to harsh environmental conditions, resulting in significant downtime and financial losses. This study presents an innovative early warning system for monitoring gearbox oil temperature using a novel FSTAE-ATT model. The system leverages SCADA data and employs Feature Mode Decomposition (FMD) to enhance feature extraction from gearbox oil temperature measurements. The FSTAE-ATT model integrates Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependencies, augmented by a self-attention mechanism to highlight critical features. The model's reconstruction error serves as an early warning indicator for gearbox oil temperature anomalies. The effectiveness of the FSTAE-ATT model was validated using real-world data from an offshore wind farm in Yangjiang, Guangdong, China. Comparative analysis with other models, including STAE, STAE-ATT, AE, TAE, and SAE, demonstrated that the FSTAE-ATT model outperforms them with lower RMSE (e.g., 0.003452 for unit #40) and MAE (e.g., 0.002828 for unit #40) metrics. Additionally, significantly earlier warning times (e.g., up to 22 h and 36 min for unit #40), provide substantial lead time for preventative maintenance. This work contributes to advancing offshore wind turbine condition monitoring and fault detection, enhancing the sustainability and profitability of offshore wind energy systems.
{"title":"The early warning method for offshore wind turbine gearbox oil temperature based on FSTAE-ATT","authors":"Anping Wan , Shuai Peng , Khalil AL-Bukhaiti , Yunsong Ji , Shidong Ma","doi":"10.1016/j.suscom.2025.101180","DOIUrl":"10.1016/j.suscom.2025.101180","url":null,"abstract":"<div><div>Offshore wind turbine gearboxes often experience malfunctions due to harsh environmental conditions, resulting in significant downtime and financial losses. This study presents an innovative early warning system for monitoring gearbox oil temperature using a novel FSTAE-ATT model. The system leverages SCADA data and employs Feature Mode Decomposition (FMD) to enhance feature extraction from gearbox oil temperature measurements. The FSTAE-ATT model integrates Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependencies, augmented by a self-attention mechanism to highlight critical features. The model's reconstruction error serves as an early warning indicator for gearbox oil temperature anomalies. The effectiveness of the FSTAE-ATT model was validated using real-world data from an offshore wind farm in Yangjiang, Guangdong, China. Comparative analysis with other models, including STAE, STAE-ATT, AE, TAE, and SAE, demonstrated that the FSTAE-ATT model outperforms them with lower RMSE (e.g., 0.003452 for unit #40) and MAE (e.g., 0.002828 for unit #40) metrics. Additionally, significantly earlier warning times (e.g., up to 22 h and 36 min for unit #40), provide substantial lead time for preventative maintenance. This work contributes to advancing offshore wind turbine condition monitoring and fault detection, enhancing the sustainability and profitability of offshore wind energy systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101180"},"PeriodicalIF":5.7,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826959","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}
The use of renewable power sources is vital for reducing the world’s reliance on limited fossil fuels, reducing the impact on climate and mitigating the losses associated with power transmission. However, renewable sources such as solar power, often suffer from fluctuations in production due to their heavy reliance on weather conditions. This can have a significant impact on their reliability, as well as an impact on the power grid. Nevertheless, these issues could be mitigated by utilizing powerful and robust forecasting models, allowing for more efficient planning and fuller utilization of the produced power. This work explores the use of artificial intelligence (AI) in order to predict the yield of photovoltaic-generated energy. Different artificial neural network architectures are explored, including recurrent neural network (RNN), gated recurrent unit (GRU), and the long short-term memory (LSTM). Additionally, attention mechanism is integrated into the best-performing model to help further improve its performance. To ensure favorable outcomes, an adapted variant of the particle swarm optimization (PSO) is introduced to optimize hyper-parameter settings of each model. Simulations with real-world data showcased promising results while the rigorous statistical analysis confirmed that the observed improvements are statistically significant. The best-performing models were subjected to feature importance analysis to help future endeavors, as well as data collection efforts. The best performing models attained an impressive normalized mean square error (MSE) and coefficient of determination () of 0.007240 and 0.894693, respectively, suggesting strong perspective for real world applications. Nonetheless, the introduction of attention mechanism did not provide further improvements to the best performing model. Lastly, this study confirmed that the modifications made to the baseline PSO strengthened the original approach, as it statistically significantly outperformed other metaheuristics.
{"title":"Exploring artificial intelligence potential in solar energy production forecasting: Methodology based on modified PSO optimized attention augmented recurrent networks","authors":"Luka Jovanovic , Nebojsa Bacanin , Aleksandar Petrovic , Miodrag Zivkovic , Milos Antonijevic , Vuk Gajic , Mahmoud Mohamed Elsayed , Mohamed Abouhawwash","doi":"10.1016/j.suscom.2025.101174","DOIUrl":"10.1016/j.suscom.2025.101174","url":null,"abstract":"<div><div>The use of renewable power sources is vital for reducing the world’s reliance on limited fossil fuels, reducing the impact on climate and mitigating the losses associated with power transmission. However, renewable sources such as solar power, often suffer from fluctuations in production due to their heavy reliance on weather conditions. This can have a significant impact on their reliability, as well as an impact on the power grid. Nevertheless, these issues could be mitigated by utilizing powerful and robust forecasting models, allowing for more efficient planning and fuller utilization of the produced power. This work explores the use of artificial intelligence (AI) in order to predict the yield of photovoltaic-generated energy. Different artificial neural network architectures are explored, including recurrent neural network (RNN), gated recurrent unit (GRU), and the long short-term memory (LSTM). Additionally, attention mechanism is integrated into the best-performing model to help further improve its performance. To ensure favorable outcomes, an adapted variant of the particle swarm optimization (PSO) is introduced to optimize hyper-parameter settings of each model. Simulations with real-world data showcased promising results while the rigorous statistical analysis confirmed that the observed improvements are statistically significant. The best-performing models were subjected to feature importance analysis to help future endeavors, as well as data collection efforts. The best performing models attained an impressive normalized mean square error (MSE) and coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.007240 and 0.894693, respectively, suggesting strong perspective for real world applications. Nonetheless, the introduction of attention mechanism did not provide further improvements to the best performing model. Lastly, this study confirmed that the modifications made to the baseline PSO strengthened the original approach, as it statistically significantly outperformed other metaheuristics.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101174"},"PeriodicalIF":5.7,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829382","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}
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