Optimization Of Energy Consumption Research Articles

Enhancing residential heating load prediction with advanced machine learning and optimization techniques

The effective prediction of heating load in residential buildings is crucial for optimizing energy consumption and enhancing thermal comfort. This study investigates three prominent regression techniques Extra Tree Regression, Decision Tree Regression, and Support Vector Regression utilized for this purpose. To improve their predictive performance, a novel metaheuristic optimization algorithm, the Tyrannosaurus Rex Optimization Algorithm, is employed. The dataset used includes features such as Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, and Glazing Area Distribution. Each machine learning model (Extra Tree Regression, Decision Tree Regression, Support Vector Regression) is trained on historical heating load data and optimized using the Tyrannosaurus Rex Optimization Algorithm to adjust internal parameters. Comparative analysis demonstrates that the proposed Extra Tree + Tyrannosaurus Rex Optimization Algorithm model achieves superior performance with an R-squared value of 0.987 and a low Root Mean Square Error of 1.167. By leveraging the strengths of different machine learning algorithms, this research presents a reliable approach for accurately predicting heating load in residential buildings. This method holds promise for improving energy efficiency, enhancing thermal comfort, and promoting sustainable building design.

Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

SummaryVehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

Prediction of residential building occupancy using Machine learning with integrated sensor and survey Data: Insights from a living lab in Morocco

Building occupancy information is essential for effective energy management in buildings through the adoption of energy conservation and occupant-centric control strategies. These strategies endeavor to contribute to optimizing energy consumption while ensuring occupant comfort. This study focuses on advanced occupancy modeling techniques to enhance energy efficiency in residential buildings, utilizing various data-driven techniques. Various Machine Learning models, such as Random Forest, Bayesian Network, Decision Trees, Support Vector Machines, K-Nearest Neighbors, eXtreme Gradient Boosting, and Regularized Greedy Forest, have been evaluated for predicting and classifying building occupancy. Employing a living lab approach within a residential setting, the research evaluates model performance using two ground truth datasets: IoT sensor and survey data. Experimental tests use Accuracy and F1_score as evaluation criteria, demonstrating accuracy rates ranging from 70% to 95.96%. The results highlight the performance of these models in predicting residential occupancy, offering valuable insight into two approaches to occupancy modeling. The Random Forest model performs exceptionally well in capturing occupancy trends, while the Bayesian Network model, combined with expert knowledge, provides detailed predictions of occupancy types and zones. The research also addresses challenges related to data collection, including privacy concerns. It presents effective occupancy modeling strategies for residential energy management.

A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total.

Analysis and Optimization of Fluid Flow on the Surface of the Prototype Body of Electrical Vehicle Ganesha Diffabilities Using Solidworks Software

Purpose: The aerodynamic aspect is an aspect that takes into account a force caused by the fluid flow shown by the Coefficient of Drag value. This will affect the optimization of performance and energy consumption used. This study aims to modify the standard design of Electrical Ganesha Diffabilities so that a design with a low Coefficient of Drag value considers ergonomic parameters and user comfort to follow the community of people with disabilities. Theoretical Framework: The main theoretical foundation is based on the laws of aerodynamics, which analyze the movement of air around solid objects and its effect on vehicle performance, with a specific emphasis on minimizing drag. The research employs computational fluid dynamics (CFD), a branch of fluid flows. Models and simulations in Solidworks offer a digital environment for improving design aspects to increase aerodynamic efficiency. This theoretical approach focuses on two main objectives: reducing the drag coefficient (CD), which measures resistance caused by shape and surface characteristics, and applying these findings to achieve practical energy savings and enhance vehicle operation specifically designed for individuals with disabilities. The integration of aerodynamic theory and practical application is essential for solving the specific transportation requirements emphasized in the introduction. This alignment with legislation and social objectives aims to provide improved support for the impaired community. Design/Methodology/Approach: This is a numerical method with an R2D2 research model (Reflective, Recursive, Design, and Development) using the 2020 version of Solidworks Software. Solidworks software is known for software that has speed and accuracy in analyzing. Findings: The results of the fluid flow analysis simulation showed that the design of the modified Electrical Ganesha diffabilities vehicle had a Coefficient of Drag value of 0.353 or 35% lower than the standard vehicle value of 0.547. Through expert assessment, the modified design of Electrical Ganesha Diffabilities has received an assessment of 98% with excellent qualifications. Research, Practical & Social implications: The research contributes to the topic of aerodynamic for disability-friendly vehicles, providing a basis for future academic investigations into accessible transportation options. Essentially, the results can immediately impact the development of electric cars that are both more energy-efficient and performance-optimized for those with impairments. This would lead to lower running expenses and improved user-friendliness. From a social perspective, this research emphasizes a dedication to inclusiveness and accessibility, which has the potential to enhance the ability of impaired persons to move around and ultimately enhance their overall well-being. Additionally, it increases awareness among policymakers and manufactures of the significance of incorporating aerodynamic efficiency into the design of assistive devices, hence fostering wider social acceptance and support for this advancement. Originality/Value: The uniqueness and significance of this work stem from its innovative strategy of combining aerodynamic principles with the development of electric cars particularly tailored for those with disabilities-a relatively unexplored field in transportation research. This research examines the E-GADIS vehicle and fills the gap in the current literature on accessible transportation options.

Low‐carbon pyrolysis of vacuum gas oil by non‐thermal plasma

AbstractThe constant growth in the share of renewable electricity and the need to reduce CO2 emissions create prospects for the development of non‐thermal plasma (NTP) pyrolysis of heavy oil. NTP pyrolysis requires only electricity and does not require heating of raw materials to high temperatures, which significantly reduces the carbon footprint during processing. This paper investigates the characteristics of NTP pyrolysis of vacuum gas oil under the action of electric microsecond discharges in the liquid phase, depending on the voltage of the current source 300–700 V to optimize energy consumption, conversion of raw materials, and increase the yield of commercial demand products.

Business Models on the Energy Market in the Era of a Low-Emission Economy

In the energy market, we observe a dynamic development of innovative business models that take into account various aspects related to the direction of zero-emission economic growth. Companies are intensifying their efforts in utilizing renewable energy sources, implementing significant photovoltaic projects, and advancing technologies related to wind and hydrodynamic energy. Within this trend, microgrids become a crucial element, enabling efficient management of local energy sources. Contemporary energy companies also focus on innovative digital technologies, harnessing the potential of the Internet of Things (IoT) and artificial intelligence (AI). These tools allow for precise monitoring and optimization of energy consumption, resulting in increased operational efficiency. The expansion of subscription-based energy services encompasses not only traditional energy deliveries but also new aspects, such as intelligent management of home energy installations or the provision of advisory services on energy conservation. This approach emphasizes the customer as a partner in sustainable energy usage. Hybrid energy models, integrating diverse energy sources, constitute a key element in the transformation of the sector. The combination of photovoltaic, wind, and traditional power plants allows for flexible adaptation to changing conditions and maintains stability in the energy supply. In the face of a changing energy landscape, companies consistently strive for sustainable practices, implementing strategies that not only reduce their carbon footprint but also contribute to improving efficiency, ecology, and the decentralization of the energy system. Adapting to these dynamic changes becomes not only a challenge but also an opportunity to create a more sustainable energy future. The objective of this research is to analyze key business models in the energy market and identify their impact on operational efficiency and market competitiveness. The main results indicate significant improvements in energy management and sustainability through the adoption of these models.

Complex artificial intelligence models for energy sustainability in educational buildings

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Methods and algorithms of swarm intelligence for the problems of nonlinear regression analysis and optimization of complex processes, objects, and systems: review and modification of methods and algorithms

The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems. Since most serious problems involve the search for tens, hundreds, or thousands of optimal parameters of mathematical models, the search space for these parameters grows non-linearly. Currently, there are many modern methods and algorithms of swarm intelligence that solve today's scientific and applied problems, but they require modifications due to the large spaces of searching for optimal model parameters. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. It can be used to solve scientific and applied problems of optimizing energy consumption in buildings, industrial complexes, and urban systems, reducing energy losses, and increasing the efficiency of resource use, as well as for the construction of various elements of energy systems in general. Well-known methods and algorithms of swarm intelligence are also actively applied to forecast energy production from renewable sources, such as solar and wind energy. This allows better management of energy sources and planning of their use. The relevance of modifications of methods and algorithms is due to the issues of speeding up their work when solving machine learning problems, in particular, in nonlinear regression models, classification, and clustering problems, where the number of observed data can reach tens and hundreds of thousands or more. The work considers and modifies well-known effective methods and algorithms of swarm intelligence (particle swarm optimization algorithm, bee optimization algorithm, differential evolution method) for finding solutions to multidimensional extremal problems with and without restrictions, as well as problems of nonlinear regression analysis. The obtained modifications of the well-known classic effective methods and algorithms of swarm intelligence, which are present in the work, effectively solve complex scientific and applied tasks of designing complex objects and systems. A comparative analysis of methods and algorithms will be conducted in the next study on this topic. Keywords: optimization, swarm intelligence, mathematical modelling, nonlinear regression, complex objects and systems.

Exploring the Application of Digital Twin Technology in the Energy Sector using MEREC and MAIRCA Methods

Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in practice, helping optimize energy efficiency and reduce costs. Integrating digital twin technology with the Multi-Criteria Decision-Making (MCDM) methods offers a novel approach to managing and optimizing energy systems in smart cities. The paper aims to create a consistent and robust approach to determining the best digital twin solution for energy systems in smart cities. The paper identifies critical factors for decision-making and establishes a method for assessing the significance of criteria using Triangular Neutrosophic Sets (TNS) through the MEthod based on Removal Effects of Criteria (MEREC) and the Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) approach. These methods are used to evaluate and prioritize multiple criteria in decision-making processes. Furthermore, the methods are combined with Triangular Neutrosophic Sets (TNS) to support decision-making for smart cities' energy systems, better accounting for the complex and uncertain nature of energy systems. A case study is conducted to apply and validate the developed methodology and perform a sensitivity analysis of the experimental results. The research outcomes indicated that the proposed methodology is robust and effective in handling the uncertainty and complexity inherent in smart cities' energy systems. The sensitivity analysis further confirms the stability and adaptability of the proposed methodology across different scenarios, making it a valuable tool for policymakers and stakeholders in the energy sector.

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

This article explores integrating digital twin technology and blockchain within smart grids to optimize energy trading among prosumers and consumers in smart nanogrids. Our platform employs a multi-objective optimization strategy, including Particle Swarm Optimization (PSO), to delineate energy trading routes between nanogrids, optimizing parameters such as route distance, surplus renewable energy, and energy power loss. Our platform ensures efficient and effective energy trading services by meticulously considering factors such as surplus energy amount, energy price, route distance, and time. The proposed digital twin-based architecture comprises seven layers, each tailored to address specific functionalities and services for energy management within smart nanogrids. At the apex lies the application layer (digital twin services), leveraging the digital twin's capabilities to optimize energy trading, manage surplus energy, and efficiently meet energy demand. This layer facilitates informed decision-making and resource optimization. Integrating a digital twin-driven architecture with a blockchain-based platform tackles challenges inherent in decentralized energy trading. The digital twin offers real-time energy resource monitoring and optimisation, ensuring efficient utilisation and autonomous decision-making. Concurrently, leveraging blockchain technology ensures secure and transparent transactions, fostering trust among participants and facilitating peer-to-peer energy exchange. Task generation, device virtualization, task mapping, scheduling on edge devices, and task assignment layers further streamline task execution and resource utilization, enhancing the efficiency of energy management processes. The predictive optimal energy control layer also orchestrates the entire architecture, enabling predictive and optimized energy control within smart nanogrids. Furthermore, the Security as a Service (SECaaS) layer enhances security and trustworthiness using blockchain technology, incorporating components such as consensus management, real-time distributed ledgers, and identity management. This layer enhances the security and transparency of energy-related transactions and data within the digital twin framework. The results showcase a remarkable 53% reduction in peak load, emphasizing the optimized energy consumption and demand achieved. Furthermore, our platform has significantly increased the utilization of renewable energy resources by 24%, highlighting its contribution to sustainable energy resource management. Rigorous assessment of the prediction and optimization modules reveals their high accuracy and precision, with mean absolute percentage error (MAPE) values of 15.125 and 14.369, respectively. These findings underscore the efficacy and reliability of our digital twin-based approach, surpassing existing solutions and benchmarks.

Eco-FL: Enhancing Federated Learning sustainability in edge computing through energy-efficient client selection

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the decentralization of machine learning (ML) models by enabling their training across multiple devices. In this way, FL preserves privacy and minimizes the need for centralized data by processing data near the source. From a communication standpoint, only the model weights are exchanged between devices. By avoiding the need to send data to a centralized location for processing, FL reduces the energy required for data transfer and supports more efficient use of computing resources at the edge. FL is particularly advantageous for resource-constrained devices, such as smartphones and IoT devices. However, this limited computational power and battery capacity and the challenge of energy consumption are critical aspects of FL systems. This paper introduces Eco-FL, an innovative methodology designed to optimize energy consumption in FL systems, in the field of Green Edge Cloud Computing (GECC). Our approach employs a device selection process that considers the entropy of the data held by the devices and their available energy reserves. This ensures that devices with lower energy availability are less likely to participate in the training rounds, prioritizing those with higher energy capacities. To evaluate the efficacy of our methodology, we utilize FedEntropy, an entropy-based aggregation method, alongside established aggregation methods such as FedAvg and FedProx for performance comparison. The effectiveness of Eco-FL in reducing energy consumption without compromising the accuracy of the FL process is demonstrated through analyses conducted on three distinct datasets. These analyses vary the β parameter of the Dirichlet distribution and account for scenarios with both homogeneous and heterogeneous initial device charges. Our findings validate Eco-FL’s potential to enhance the sustainability of FL systems by judiciously managing client participation based on energy criteria, presenting a significant step forward in the development of energy-efficient FL.

The Impact of the Digital Economy on Provincial Carbon Productivity: Empirical Evidence from China

With the introduction of the dual carbon goals of “carbon peak” and “carbon neutrality”, low-carbon economic development has become the focus of attention for all sectors in China. Improving carbon productivity (CP) is the key to developing a low-carbon economy and is essential to achieving carbon emission reduction and economic growth. The rapid development of the digital economy (DE) provides a new breakthrough for improving carbon productivity, but there are few studies on the digital economy and carbon productivity in the existing literatures. We empirically investigate the impact mechanism of the digital economy on provincial carbon productivity using China’s provincial panel data from 2010 to 2019. This study finds that the digital economy significantly raises provincial carbon productivity; this result remains valid after a series of robustness tests. The digital economy can impact provincial carbon productivity through both energy consumption structure (ECS) and green technology innovation (GTI) and can contribute to the improvement of provincial carbon productivity by optimizing energy consumption structure and improving green technology innovation. In addition, different environmental regulations have different impacts. Command-and-control environmental regulation (CER) weakens the positive impact of the digital economy on provincial carbon productivity, whereas market-based environmental regulation (MER) enhances the positive impact of the digital economy on provincial carbon productivity. This study extends the research on the digital economy in the field of low-carbon development, providing insights and policy suggestions for relevant departments to promote low-carbon development from the perspective of the digital economy.

Optimization of Energy Consumption in Formaldehyde Production Process Using Reboiled Absorption Process

Formaldehyde is a crucial chemical building block in various industries, and its production often involves energy-intensive processes. This study focuses on optimizing energy consumption in formaldehyde production, explicitly employing the reboiled absorption process with a production capacity of 27,000 tons per year. The objective of this article is to develop a more energy-efficient process of formaldehyde synthesis by addition or refrigerant cycle stream to preserve the energy, reducing energy consumption and improving the sustainability of the process. The reboiled absorption process involves the absorption of formaldehyde gas into a liquid absorbent, followed by reboiling to release the absorbed formaldehyde. A comprehensive analysis of the entire production system compares unmodified and modified process simulations, heat integration, and energy analysis. Beside the energy consumption of the process, the number of stages within the absorption process contributes to the product mass flow rate of the overall process by increasing the surface area which mass transfer can occur. However, adding too many stages to the process may negatively impact the energy efficiency of the process. Therefore, optimizing energy consumption and absorption processes in formaldehyde production is essential to improve the sustainability of the process and increase the overall profitability of the production process. The results show that the proposed method dramatically improves the sustainability of CH2O synthesis by reducing overall energy consumption and emissions by 93.978%, reducing energy consumption from 153,735,360.4 kJ/h to 9,256,646.618 kJ/h. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

А comprehensive approach to managing robot group formation

Subject matter: Research and development of methods for controlling swarms of unmanned aerial vehicles (UAVs) based on the "master – slave" model. This includes examining existing classifications and interactions between unmanned aerial vehicles in various formations such as groups, flocks, associations, and swarms, with the goal of creating an effective management system. Goal To improve the quality of interaction between unmanned aerial vehicles based on the "master – slave" model during flight missions through constant control between objects. Ensuring reliable execution of flight missions by implementing new management methods that account for different modes of interaction between devices. Tasks: Analyze the classification of existing UAVs; analyze the parameters and model of interaction of unmanned aerial vehicles in existing groups, flocks, associations, swarms; create a scenario of interaction between two UAVs based on the "master – slave" model; develop a program for visualizing the flight of unmanned aerial vehicles based on the "master – slave" model; conduct flight testing according to the proposed model on stages with various geospatial objects. Methods: Simulation method for developing a UAV flight visualization subsystem; graphical modeling method for creating an aircraft-type unmanned aerial vehicle model; methods of algorithm theory for developing a scenario of interaction between two UAVs. Utilization of specialized software tools for visualization and simulation of UAV behavior in real-time conditions. Results: Developed a classification of unmanned aerial vehicles; created a graphical model of the Mini-Flight-M aircraft; developed a scheme for the interaction of two UAVs in "teacher" or "mentor" modes; created a program for visualizing the flight of UAVs based on the "master – slave" model; conducted flight testing according to the proposed model on stages with various geospatial objects. The results confirmed the effectiveness of the developed model and demonstrated its applicability in various fields, including environmental monitoring, rescue operations, and other autonomous missions. Conclusions: The proposed approach to controlling a UAV swarm based on the "master – slave" model improves the quality of interaction between the devices and ensures reliable execution of flight missions. Further research should focus on optimizing energy consumption and ensuring reliable communication between swarm agents. It is also important to develop methods for protecting UAV swarms from cyberattacks and other threats to enhance their resilience and reliability during complex missions.

Toward Enhanced Efficiency: Soft Sensing and Intelligent Modeling in Industrial Electrical Systems

This review article focuses on applying operation state detection and performance optimization techniques in industrial electrical systems. A comprehensive literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to ensure a rigorous and transparent selection of high-quality studies. The review examines in detail how soft sensing technologies, such as state estimation and Kalman filtering, along with hybrid intelligent modeling techniques, are being used to enhance efficiency and reliability in the electrical industry. Specific case studies are analyzed in areas such as electrical network monitoring, fault detection in high-voltage equipment, and energy consumption optimization in industrial plants. The PRISMA methodology facilitated the identification and synthesis of the most relevant studies, providing a robust foundation for this review. Additionally, the article explores the challenges and research opportunities in applying these techniques in specific industrial contexts, such as steel metallurgy and chemical engineering. By incorporating findings from meticulously selected studies, this work offers a detailed, engineering-oriented insight into how advanced technologies are transforming industrial processes to achieve greater efficiency and operational safety.

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

AN EVALUATION METHOD OF AN ENERGY CONSUMPTION AS AN OPERATION PARAMETER IN A CYBER-PHYSICAL SYSTEM

The research of energy consumption in an Internet of Things network and its analytical evaluation is the goal of this work. The authors of this work concentrate on developing a model for calculating the actual gain in power consumption in order to estimate the actual energy required. The method suggests measuring the difference in energy usage under three primary battery-powered working modes to maximize a device's lifetime. Due to the fact that each CPS device state has its own energy metrics, it is feasible to choose the best operation course for entire network. The presented technique is certainly viable, as demonstrated by the experimental examination of Zigbee and BLE devices. The comparison of power levels using a temperature sensor in three basic scenarios (power modes) dictates how the CPS device lifetime can be optimized. Multi-regime consumption models, in which the rates of charging and discharging are dependent upon the energy level, are analyzed in this paper. This work aimed to state an optimal energy consumption by finding the right balance between operational power and battery lifetime through mathematical modeling. Therefore, it is easy to determine the energy cost of power stage, for instance, to send data by setting the minimal duration of each working condition in terms of power consumption. Moreover, a reasonable balance of power consumption and battery lifetime which impacts the data collection from sensors is vital to the development of data extraction algorithms. The practical results depict how device should be accessible to be able to lose less power even during switching on/off or how operate more effective if it used for a short period of time. A long-term network could become a reality once battery life is optimized enough to not disturb a user.

ENHANCING EFFICIENCY WITH ARTIFICIAL INTELLIGENCE IN THE TRANSITION TO A GREEN DIGITAL ECONOMY

This article examines the role of artificial intelligence (AI) in advancing Uzbekistan's green digital economy, following President Shavkat Mirziyoyev's 2021 decree. It highlights AI's contributions to optimizing energy consumption, developing smart infrastructure, and integrating renewable energy. Challenges such as education, infrastructure development, and policy are discussed. Through case studies on Tashkent's smart city projects, solar energy initiatives, and precision farming, the article demonstrates AI's benefits for sustainability and economic growth. The article concludes by emphasizing the significant economic and environmental impacts of AI in Uzbekistan's transition to a green digital economy.

Unsteady load alleviation on highly flexible bio-inspired wings in longitudinally oscillating freestreams

This study delves into the aerodynamic behaviour of a highly flexible NACA 0012 aerofoil, drawing inspiration from avian feathers to handle a gust. We first examined unsteady flow on a rigid wing both experimentally and numerically and then explored the implications of introducing wing flexibility purely numerically. Our findings underscore the potential of composite materials in alleviating the oscillating aerodynamic forces on a wing under a streamwise gust. This behaviour is attributed to its capacity to destabilize the laminar separation bubble, fostering a more stable turbulent boundary layer. While direct avian evidence remains limited, it is postulated that in nature, such mechanisms could mitigate undesired wing flapping, optimizing energy consumption in perturbed environments.