Pub Date : 2025-12-01Epub Date: 2025-09-22DOI: 10.1016/j.suscom.2025.101207
Ercan Erkalkan
This study addresses renewable-energy storage scheduling — a high-dimensional, multimodal optimization task — by proposing an enhanced Grey Wolf–Whale Optimization Algorithm (EGW–WOA). The method fuses GWO’s hierarchical leadership with WOA’s spiral exploitation and augments them with Lévy flights and progress-triggered chaotic re-initialization. Across 100 Monte-Carlo trials, EGW–WOAreduced 24 h operating cost to , improving over WOA by 16.62%, GA by 10.15%, FPA by 63.6%, and HS by 80.76%, with a 100% feasibility rate. It achieved the lowest dispersion (Std ; Max–Min spread ), shaved peak-demand charges by 9%, and limited depth-of-discharge swings to %, projecting a 12%–18% life extension. A 50-iteration run completed in 38.6 s on a 3.4 GHz CPU — over faster than a comparable MILP baseline — demonstrating suitability for near-real-time PV–wind microgrid control. Within the scope of Sustainable Computing: Informatics and Systems, this work delivers a reproducible, open-source optimization engine with non-parametric statistical validation and edge-suitable runtimes, linking algorithmic advances to system-level sustainability metrics (LCOS, demand charges). The results show how algorithm–system co-design can lower operating cost and risk while preserving battery health in cyber–physical energy systems.
{"title":"An enhanced hybrid optimization model for renewable energy storage: Integrating GWO and WOA, with Lévy mechanisms","authors":"Ercan Erkalkan","doi":"10.1016/j.suscom.2025.101207","DOIUrl":"10.1016/j.suscom.2025.101207","url":null,"abstract":"<div><div>This study addresses renewable-energy storage scheduling — a high-dimensional, multimodal optimization task — by proposing an enhanced Grey Wolf–Whale Optimization Algorithm (EGW–WOA). The method fuses GWO’s hierarchical leadership with WOA’s spiral exploitation and augments them with Lévy flights and progress-triggered chaotic re-initialization. Across 100 Monte-Carlo trials, EGW–WOAreduced 24<!--> <!-->h operating cost to <span><math><mrow><mn>2</mn><mo>.</mo><mn>94</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup><mo>±</mo><mn>7</mn><mo>.</mo><mn>97</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span>, improving over WOA by 16.62%, GA by 10.15%, FPA by 63.6%, and HS by 80.76%, with a 100% feasibility rate. It achieved the lowest dispersion (Std <span><math><mrow><mo>=</mo><mn>7</mn><mo>.</mo><mn>97</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span>; Max–Min spread <span><math><mrow><mo>=</mo><mn>3</mn><mo>.</mo><mn>82</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span>), shaved peak-demand charges by <span><math><mo>≈</mo></math></span>9%, and limited depth-of-discharge swings to <span><math><mrow><mo><</mo><mn>35</mn></mrow></math></span>%, projecting a 12%–18% life extension. A 50-iteration run completed in 38.6<!--> <!-->s on a 3.4<!--> <!-->GHz CPU — over <span><math><mrow><mn>20</mn><mo>×</mo></mrow></math></span> faster than a comparable MILP baseline — demonstrating suitability for near-real-time PV–wind microgrid control. Within the scope of <em>Sustainable Computing: Informatics and Systems</em>, this work delivers a reproducible, open-source optimization engine with non-parametric statistical validation and edge-suitable runtimes, linking algorithmic advances to system-level sustainability metrics (LCOS, demand charges). The results show how algorithm–system co-design can lower operating cost and risk while preserving battery health in cyber–physical energy systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101207"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158339","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 : 2025-12-01Epub Date: 2025-09-21DOI: 10.1016/j.suscom.2025.101212
Venkata Ramana Gupta Nallagattla , Amrita Rai , S. Thangam , G. Joel Sunny Deol , Abburi Srirama Kanaka Ratnam , J. Nageswara Rao , A. Santhi Mary Antony , K. Balasubramanian , Shamimul Qamar
In a rapidly urbanizing environment, cities have changed into complicated ecosystems requiring sophisticated technological solutions to resolve excessive traffic, energy utilization, waste management, and public safety issues. This study discusses a single architecture for IoT-enabled smart cities through the use of blockchain enabled security, energy efficient machine learning, real-time analytics, and decision-making to overcome scalability, interoperability, and security issues generally present in a smart infrastructure. The framework utilizes lightweight algorithm-based cost-effective computation, integration of heterogeneous IoT devices, real-time decision making, transparency, and involvement of stakeholders. The simulation findings show substantial advantages over traditional methods: a 35 % decrease in processing latency; a 25 % decrease in energy consumption; and a 29 % increase in an index for data security. Also, predictive analytics exhibited over 90 % identification accuracy across the different urban contexts, including traffic control for improved public safety, and environmental monitoring/management scenarios that ensured reliable forecasted events and appropriate resource allocation. The blockchain module demonstrated median transaction validation times of less than 2 ms to validate IoT data streams enabling real-time secure operations even under demanding environmental conditions. Also, we achieved resource allocation optimization with efficiencies that exceeded 85 % for designated priority supplies, including food, energy, medical resources, and reduced waste and improved disaster resilience. This model is adaptable across different urban settings and is a scalable, secure, and energy efficient framework for the next generation of smart cities contributing to sustainable urbanization and improved quality of urban life.
{"title":"Blockchain-enabled IoT framework with energy-efficient machine learning for scalable and secure smart cities","authors":"Venkata Ramana Gupta Nallagattla , Amrita Rai , S. Thangam , G. Joel Sunny Deol , Abburi Srirama Kanaka Ratnam , J. Nageswara Rao , A. Santhi Mary Antony , K. Balasubramanian , Shamimul Qamar","doi":"10.1016/j.suscom.2025.101212","DOIUrl":"10.1016/j.suscom.2025.101212","url":null,"abstract":"<div><div>In a rapidly urbanizing environment, cities have changed into complicated ecosystems requiring sophisticated technological solutions to resolve excessive traffic, energy utilization, waste management, and public safety issues. This study discusses a single architecture for IoT-enabled smart cities through the use of blockchain enabled security, energy efficient machine learning, real-time analytics, and decision-making to overcome scalability, interoperability, and security issues generally present in a smart infrastructure. The framework utilizes lightweight algorithm-based cost-effective computation, integration of heterogeneous IoT devices, real-time decision making, transparency, and involvement of stakeholders. The simulation findings show substantial advantages over traditional methods: a 35 % decrease in processing latency; a 25 % decrease in energy consumption; and a 29 % increase in an index for data security. Also, predictive analytics exhibited over 90 % identification accuracy across the different urban contexts, including traffic control for improved public safety, and environmental monitoring/management scenarios that ensured reliable forecasted events and appropriate resource allocation. The blockchain module demonstrated median transaction validation times of less than 2 ms to validate IoT data streams enabling real-time secure operations even under demanding environmental conditions. Also, we achieved resource allocation optimization with efficiencies that exceeded 85 % for designated priority supplies, including food, energy, medical resources, and reduced waste and improved disaster resilience. This model is adaptable across different urban settings and is a scalable, secure, and energy efficient framework for the next generation of smart cities contributing to sustainable urbanization and improved quality of urban life.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101212"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158256","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 intelligent control issue of renewable energy dispatching in micro grids relates to the scenario of energy efficiency and transaction security. An innovative framework is introduced on the real-time scheduling of solar and wind energy over a distributed network that includes DRL-based controllers embedded in a blockchain-authenticated dispatch-protocol. DRA PPO is applied by the DRA agent to optimize strategies of power distribution dynamically across multiple prosumer nodes under the influence of stochastic generation and consumption profiles. The blockchain layer is specifically made to validate dispatch decision with the help of smart contracts, which guarantee integrity of data, tamper-proof scheduling, and transparent peer-to-peer energy exchange. An OPAL-RT + Hyperledger Fabric testbed was experimentally validated to 96.8 Renewable Dispatch Accuracy, 19.5 Energy Loss Reduction and 14.3 Grid Stability Improvement and transaction finality is within 2.1 s. Economic analysis also denoted a 25 % cost saving by prosumers relative to rule-based control. This decentralized control architecture has therefore been proven to be scalable to heterogeneous groups of microgrids, resilient to node failure, or cyber-attacks. Combination of DAR with blockchain creates a safe, self-reinforcing, and energy efficient attention framework perfectly fit in the coming generation of green energy dispatch frameworks.
{"title":"Energy efficient optimization of renewable energy dispatch using blockchain-verified deep reinforcement learning controllers","authors":"Murugan Marimuthu , Padmaja Kadiri , Senthilkumar Ganapathy , Venkatesh Kumar Pandiyan","doi":"10.1016/j.suscom.2025.101256","DOIUrl":"10.1016/j.suscom.2025.101256","url":null,"abstract":"<div><div>The intelligent control issue of renewable energy dispatching in micro grids relates to the scenario of energy efficiency and transaction security. An innovative framework is introduced on the real-time scheduling of solar and wind energy over a distributed network that includes DRL-based controllers embedded in a blockchain-authenticated dispatch-protocol. DRA PPO is applied by the DRA agent to optimize strategies of power distribution dynamically across multiple prosumer nodes under the influence of stochastic generation and consumption profiles. The blockchain layer is specifically made to validate dispatch decision with the help of smart contracts, which guarantee integrity of data, tamper-proof scheduling, and transparent peer-to-peer energy exchange. An OPAL-RT + Hyperledger Fabric testbed was experimentally validated to 96.8 Renewable Dispatch Accuracy, 19.5 Energy Loss Reduction and 14.3 Grid Stability Improvement and transaction finality is within 2.1 s. Economic analysis also denoted a 25 % cost saving by prosumers relative to rule-based control. This decentralized control architecture has therefore been proven to be scalable to heterogeneous groups of microgrids, resilient to node failure, or cyber-attacks. Combination of DAR with blockchain creates a safe, self-reinforcing, and energy efficient attention framework perfectly fit in the coming generation of green energy dispatch frameworks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101256"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684123","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 : 2025-12-01Epub Date: 2025-08-05DOI: 10.1016/j.suscom.2025.101179
Azath Mubarakali , Asma AlJarullah
The Internet of Things (IoT) is used in healthcare to monitor patients via wearable sensors to measure different physiological parameters. Smart healthcare IoT-enabled sensors and medical device data collaborate with other smart devices to transfer collected sensitive healthcare data to the central server in a secure manner. However, this collected data suffers from noise, imbalance, privacy concerns, and challenges in real-time analysis. Thus, this work is to develop a novel IoT and Explainable Artificial Intelligence (XAI) based data aggregation framework in smart healthcare systems to enable accurate patient health status and decision-making in real-time. Initially, body-integrated wearable sensors and devices collect physiological data, forming a comprehensive dataset. After that, this data is preprocessed and encrypted using Fully Homomorphic Encryption for secure transmission to the centralized servers. Meaningful features are extracted from the preprocessed data using Autoencoders, which perform effective dimensionality reduction while preserving critical information. Finally, Tabular Network (TabNet) classifies health status and risks with high precision. TabNet is a deep learning model specifically designed for structured data, which efficiently handles tabular data using attention mechanisms for feature selection and decision-making. The framework integrates XAI methods to provide interpretable predictions and actionable insights, ensuring transparency for healthcare providers. As a result, TabNet demonstrates a remarkable accuracy rate of 99.57 %, making it possible for doctors to provide consultations at any time, thereby improving the efficiency of traditional medical systems.
{"title":"IoT and XAI-driven data aggregation framework for intelligent decision-making in smart healthcare systems","authors":"Azath Mubarakali , Asma AlJarullah","doi":"10.1016/j.suscom.2025.101179","DOIUrl":"10.1016/j.suscom.2025.101179","url":null,"abstract":"<div><div>The Internet of Things (IoT) is used in healthcare to monitor patients via wearable sensors to measure different physiological parameters. Smart healthcare IoT-enabled sensors and medical device data collaborate with other smart devices to transfer collected sensitive healthcare data to the central server in a secure manner. However, this collected data suffers from noise, imbalance, privacy concerns, and challenges in real-time analysis. Thus, this work is to develop a novel IoT and Explainable Artificial Intelligence (XAI) based data aggregation framework in smart healthcare systems to enable accurate patient health status and decision-making in real-time. Initially, body-integrated wearable sensors and devices collect physiological data, forming a comprehensive dataset. After that, this data is preprocessed and encrypted using Fully Homomorphic Encryption for secure transmission to the centralized servers. Meaningful features are extracted from the preprocessed data using Autoencoders, which perform effective dimensionality reduction while preserving critical information. Finally, Tabular Network (TabNet) classifies health status and risks with high precision. TabNet is a deep learning model specifically designed for structured data, which efficiently handles tabular data using attention mechanisms for feature selection and decision-making. The framework integrates XAI methods to provide interpretable predictions and actionable insights, ensuring transparency for healthcare providers. As a result, TabNet demonstrates a remarkable accuracy rate of 99.57 %, making it possible for doctors to provide consultations at any time, thereby improving the efficiency of traditional medical systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101179"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893611","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":"2025-12-01","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 : 2025-12-01Epub Date: 2025-11-13DOI: 10.1016/j.suscom.2025.101254
Chen Tao
Accurate image recognition and classification in automated decision-making systems needs a significant deep learning model with an ability of managing the large volumetric Data. The traditional convolutional model is often fails to captures the spatial dependencies in the image, that limiting the accuracy of the model in several domains. Convolutional neural networks (CNNs) characterize a period of deep learning processes frequently applied in computer vision, which may be used to examine images and assign learnable weights to distinct objects in the image. This study compares the 3D V-Net, YOLOv4-EfficientNet, Grad-CAM, Gabor CNN, and Deep Feedforward Network deep learning model advancements and evaluates the most reliable model for robust image recognition in decision-making systems in various domains. The concert of each model is tested by means of performance metrics that includes, Precision, F1 Score, Recall, Accuracy, Intersection over Union (IoU), Dice Coefficient, Mean Squared Error (MSE), mean Average Precision (mAP) and Mean Absolute Error (MAE). Comparative analysis showcases that, the 3D V-Net mode surpasses the other models in by achieving the higher IoU of 85.4 % and Dice Coefficient of 90.3 %) whereas the Gabor CNN balances accuracy and the computational efficiency. The YOLOv4-EfficientNet and Grad-CAM offers a transparency in classification decisions. The results showcase that, the selected model is determined by application demands, with the 3D V-Net remains a most significant for image recognition and automated decision-making systems.
{"title":"An energy-efficient deep learning model evaluation for robust image recognition in automated decision-making systems","authors":"Chen Tao","doi":"10.1016/j.suscom.2025.101254","DOIUrl":"10.1016/j.suscom.2025.101254","url":null,"abstract":"<div><div>Accurate image recognition and classification in automated decision-making systems needs a significant deep learning model with an ability of managing the large volumetric Data. The traditional convolutional model is often fails to captures the spatial dependencies in the image, that limiting the accuracy of the model in several domains. Convolutional neural networks (CNNs) characterize a period of deep learning processes frequently applied in computer vision, which may be used to examine images and assign learnable weights to distinct objects in the image. This study compares the 3D V-Net, YOLOv4-EfficientNet, Grad-CAM, Gabor CNN, and Deep Feedforward Network deep learning model advancements and evaluates the most reliable model for robust image recognition in decision-making systems in various domains. The concert of each model is tested by means of performance metrics that includes, Precision, F1 Score, Recall, Accuracy, Intersection over Union (IoU), Dice Coefficient, Mean Squared Error (MSE), mean Average Precision (mAP) and Mean Absolute Error (MAE). Comparative analysis showcases that, the 3D V-Net mode surpasses the other models in by achieving the higher IoU of 85.4 % and Dice Coefficient of 90.3 %) whereas the Gabor CNN balances accuracy and the computational efficiency. The YOLOv4-EfficientNet and Grad-CAM offers a transparency in classification decisions. The results showcase that, the selected model is determined by application demands, with the 3D V-Net remains a most significant for image recognition and automated decision-making systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101254"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568408","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 : 2025-12-01Epub Date: 2025-11-27DOI: 10.1016/j.suscom.2025.101265
Abdullah M. Shaheen , Ali M. El-Rifaie , Badr Al Faiya , Ghareeb Moustafa , Hashim Alnami
Large-scale optimization in Combined Economic and Environmental dispatch (CEED) is crucial for improving electrical power system management. This study introduces a developed weIghted meaN oF vectOrs Technique (INFOT) algorithm tailored for the CEED problem, featuring three primary operators, vector combining, rule updating, and local searching, that collaboratively optimizes generation costs and reduces environmental emissions. Addi The developed INFOT algorithm is utilized to solve the CEED problem and tested on two large scale power system with 40 and 160 thermal units. The INFOT algorithm is compared with several recent optimization techniques. For the 40-unit power generation system with a load demand of 10,500 MW, the proposed INFOT algorithm achieves a 5.6 % reduction in total fuel costs in cost minimization (Scenario 1) compared to the best competitor, while showing a significant improvement in emissions. Specifically, INFOT reduces emissions from 386,946 kg/h in Scenario 1–200,138.8 kg/h in costs and emissions minimization (Scenario 2), representing a 48.3 % reduction. Additionally, the generator output analysis indicates that INFOT can balance the generation requirements, preventing excessive stress on any particular unit and improving overall system stability. The study confirms that INFOT is a competitive and reliable optimization method for addressing CEED problems, effectively managing load variations and generator outputs over a 24-hour period. To validate its practical applicability, the proposed INFOT algorithm was applied to the IEEE 30-bus system for emission minimization. Comparative results demonstrate INFOT’s superior convergence speed and lowest emission levels relative to several state-of-the-art algorithms.
{"title":"Economic and environmental optimization-dispatch in large-scale power systems using weighted mean of vectors algorithm","authors":"Abdullah M. Shaheen , Ali M. El-Rifaie , Badr Al Faiya , Ghareeb Moustafa , Hashim Alnami","doi":"10.1016/j.suscom.2025.101265","DOIUrl":"10.1016/j.suscom.2025.101265","url":null,"abstract":"<div><div>Large-scale optimization in Combined Economic and Environmental dispatch (CEED) is crucial for improving electrical power system management. This study introduces a developed weIghted meaN oF vectOrs Technique (INFOT) algorithm tailored for the CEED problem, featuring three primary operators, vector combining, rule updating, and local searching, that collaboratively optimizes generation costs and reduces environmental emissions. Addi The developed INFOT algorithm is utilized to solve the CEED problem and tested on two large scale power system with 40 and 160 thermal units. The INFOT algorithm is compared with several recent optimization techniques. For the 40-unit power generation system with a load demand of 10,500 MW, the proposed INFOT algorithm achieves a 5.6 % reduction in total fuel costs in cost minimization (Scenario 1) compared to the best competitor, while showing a significant improvement in emissions. Specifically, INFOT reduces emissions from 386,946 kg/h in Scenario 1–200,138.8 kg/h in costs and emissions minimization (Scenario 2), representing a 48.3 % reduction. Additionally, the generator output analysis indicates that INFOT can balance the generation requirements, preventing excessive stress on any particular unit and improving overall system stability. The study confirms that INFOT is a competitive and reliable optimization method for addressing CEED problems, effectively managing load variations and generator outputs over a 24-hour period. To validate its practical applicability, the proposed INFOT algorithm was applied to the IEEE 30-bus system for emission minimization. Comparative results demonstrate INFOT’s superior convergence speed and lowest emission levels relative to several state-of-the-art algorithms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101265"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614645","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 : 2025-12-01Epub Date: 2025-11-09DOI: 10.1016/j.suscom.2025.101251
Sridhar Patthi , M. Karthiga , Kumari Priyanka Sinha , Suresh Kumar Mandala , Peruri Venkata Anusha , L. Bhagyalakshmi , P. Sreelatha , Manjunathan Alagarsamy
The integration of cutting-edge technologies like IoT, blockchain, artificial intelligence (AI) has transformed precision environmental management by making it safe, scalable and effective. The study tackles the urgent problem of a fragmented, inefficient, and wasteful environmental monitoring and management system in urban greening, and agriculture. Many of the existing monitoring and management systems lack interoperability, responsiveness in the field in real-time, responsiveness to data security, and reactivity to environmental conditions. To address these challenges, the work illustrates a unified and smart framework that applies IoT, blockchain, and AI to support autonomously altered, secure, and efficient management of ecosystems. The most significant goals are to provide more data security, decision automation, and maximizing resource utilization. Smart contracts are used to automate tasks like irrigation and regulating the temperature to deliver fast and accurate response. Scalability and versatility of the framework is illustrated in the application of the framework in various environments. Most significant results show significant enhancements in both efficiency and sustainability. The model reduced water and energy consumption by 30 % and vegetation health indices by 15 %. The blockchain integration guaranteed data integrity and zero tampering while AI-powered analytics decreased response times to less than one second. These findings reveal the model’s potential to revolutionize resource allocation in smart cities and agriculture. The major contribution of this work is establishing and verifying an integrated IoT-blockchain-AI framework, which provides not just a secure and real-time control of environmental monitoring and management, while demonstrating improved efficiency, sustainability, and scalability.
{"title":"Integration of Internet of Things blockchain and artificial intelligence for scalable and secure precision environmental management","authors":"Sridhar Patthi , M. Karthiga , Kumari Priyanka Sinha , Suresh Kumar Mandala , Peruri Venkata Anusha , L. Bhagyalakshmi , P. Sreelatha , Manjunathan Alagarsamy","doi":"10.1016/j.suscom.2025.101251","DOIUrl":"10.1016/j.suscom.2025.101251","url":null,"abstract":"<div><div>The integration of cutting-edge technologies like IoT, blockchain, artificial intelligence (AI) has transformed precision environmental management by making it safe, scalable and effective. The study tackles the urgent problem of a fragmented, inefficient, and wasteful environmental monitoring and management system in urban greening, and agriculture. Many of the existing monitoring and management systems lack interoperability, responsiveness in the field in real-time, responsiveness to data security, and reactivity to environmental conditions. To address these challenges, the work illustrates a unified and smart framework that applies IoT, blockchain, and AI to support autonomously altered, secure, and efficient management of ecosystems. The most significant goals are to provide more data security, decision automation, and maximizing resource utilization. Smart contracts are used to automate tasks like irrigation and regulating the temperature to deliver fast and accurate response. Scalability and versatility of the framework is illustrated in the application of the framework in various environments. Most significant results show significant enhancements in both efficiency and sustainability. The model reduced water and energy consumption by 30 % and vegetation health indices by 15 %. The blockchain integration guaranteed data integrity and zero tampering while AI-powered analytics decreased response times to less than one second. These findings reveal the model’s potential to revolutionize resource allocation in smart cities and agriculture. The major contribution of this work is establishing and verifying an integrated IoT-blockchain-AI framework, which provides not just a secure and real-time control of environmental monitoring and management, while demonstrating improved efficiency, sustainability, and scalability.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101251"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520043","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 : 2025-12-01Epub Date: 2025-09-08DOI: 10.1016/j.suscom.2025.101206
Özlem Sabuncu , Bülent Bilgehan
Energy efficiency in Unmanned Aerial Vehicles (UAVs) is crucial for operations, where effective payload delivery, stabilization, and communication are essential. This study presents a nonlinear energy consumption model tailored for UAVs, built upon exponential scaling and multiplicative calculus to reflect the interdependencies among payload weight, wind speed, altitude, velocity and communication power. Unlike conventional approaches that rely on linear or polynomial formulations, the proposed method incorporates energy demands from integrated systems, focusing on energy consumption. The proposed multiplicative model provides valuable insights into the energy trade-offs influenced by changing environmental and operational conditions. It improves the practicality of using UAVs for real-time aid delivery, resource allocation, and communication in challenging, resource-constrained environments, offering better accuracy than traditional energy consumption models. Validation using experimental datasets demonstrates that the proposed model achieves an 85 % improvement in accuracy compared to the recently established cubic polynomial model for predicting energy consumption. The effectiveness of the proposed multiplicative model was evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) as performance metrics. The basic polynomial model recorded an MSE of 57.4269, while the parametric polynomial model significantly improved this to 5.7794. In comparison, the multiplicative model demonstrated superior accuracy, achieving a markedly lower MSE of 0.8472. Consistently, the multiplicative model also outperformed the others in terms of RMSE, attaining the lowest value of 0.9205, thereby confirming its robustness and predictive reliability. The Mean Absolute Error (MAE) was reduced from 6.44 to 0.73, representing an 88.66 % improvement. Furthermore, the R² value increased from 0.95 to 0.99, indicating a stronger fit between the predicted and actual data. These results underscore the multiplicative model's robustness, accuracy, and reliability, demonstrating its strong potential for real-world predictive applications. The findings demonstrate that the proposed model more accurately represents energy consumption, providing a robust foundation for precise analysis and design.
{"title":"Nonlinear energy modeling for UAVs in critical missions using multiplicative calculus","authors":"Özlem Sabuncu , Bülent Bilgehan","doi":"10.1016/j.suscom.2025.101206","DOIUrl":"10.1016/j.suscom.2025.101206","url":null,"abstract":"<div><div>Energy efficiency in Unmanned Aerial Vehicles (UAVs) is crucial for operations, where effective payload delivery, stabilization, and communication are essential. This study presents a nonlinear energy consumption model tailored for UAVs, built upon exponential scaling and multiplicative calculus to reflect the interdependencies among payload weight, wind speed, altitude, velocity and communication power. Unlike conventional approaches that rely on linear or polynomial formulations, the proposed method incorporates energy demands from integrated systems, focusing on energy consumption. The proposed multiplicative model provides valuable insights into the energy trade-offs influenced by changing environmental and operational conditions. It improves the practicality of using UAVs for real-time aid delivery, resource allocation, and communication in challenging, resource-constrained environments, offering better accuracy than traditional energy consumption models. Validation using experimental datasets demonstrates that the proposed model achieves an 85 % improvement in accuracy compared to the recently established cubic polynomial model for predicting energy consumption. The effectiveness of the proposed multiplicative model was evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) as performance metrics. The basic polynomial model recorded an MSE of 57.4269, while the parametric polynomial model significantly improved this to 5.7794. In comparison, the multiplicative model demonstrated superior accuracy, achieving a markedly lower MSE of 0.8472. Consistently, the multiplicative model also outperformed the others in terms of RMSE, attaining the lowest value of 0.9205, thereby confirming its robustness and predictive reliability. The Mean Absolute Error (MAE) was reduced from 6.44 to 0.73, representing an 88.66 % improvement. Furthermore, the R² value increased from 0.95 to 0.99, indicating a stronger fit between the predicted and actual data. These results underscore the multiplicative model's robustness, accuracy, and reliability, demonstrating its strong potential for real-world predictive applications. The findings demonstrate that the proposed model more accurately represents energy consumption, providing a robust foundation for precise analysis and design.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101206"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060496","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 : 2025-12-01Epub 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":"2025-12-01","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}
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