Efficient Energy Management Research Articles

The effect of failure on energy efficiency in maritime vessels autopilot systems

The increasing demands for energy efficiency and environmental sustainability in the maritime industry have underscored the critical importance of optimizing autopilot systems, which, despite their significance, are often overlooked in ship energy efficiency management. The objective of this study is to enhance the energy efficiency of ships by focusing on the efficiency of autopilot systems, which play a significant role in the management of energy efficiency. The research emphasizes the need for effective decision support systems for ship operators, not only for optimizing ship speeds but also for making informed operational decisions. By utilizing Fuzzy Fault Tree Analysis (FFTA), the study identifies and prioritizes the causes of efficiency losses in autopilot systems and examines their frequency. Based on expert opinions, the research delves into the complexity of autopilot systems and the interactions among various components. Notably, the study highlights the impact of multiple factors on the efficiency of complex autopilot systems, elucidating their relationships through Minimum Cut Sets (MCS) analysis. Furthermore, attention was drawn to the “Improper Alarm Input” event caused by insufficient knowledge and awareness among ship operators, which hinders the effective use of autopilot systems. The findings demonstrate that decision support systems can increase energy efficiency and contribute to the reduction of operational errors by reducing the human factor, which is 99% effective on the “Inefficient Heading Control System”. Additionally, proper utilization of autopilot systems can lead to a decrease in a ship's carbon footprint and operating costs. Overall, the results can affect strategic decisions in ship energy efficiency management and encourage significant steps toward achieving International Maritime Organization's (IMO) sustainability objectives.

Application of Non-Parametric and Forecasting Models for the Sustainable Development of Energy Resources in Brazil

To achieve Sustainable Development Goal 7 (SDG7) and improve energy management efficiency, it is essential to develop models and methods to forecast and enhance the process accurately. These tools are crucial in shaping the national policymakers’ strategies and planning decisions. This study utilizes data envelopment analysis (DEA) and bootstrap computational methods to evaluate Brazil’s energy efficiency from 2004 to 2023. Additionally, it compares seasonal autoregressive integrated moving average (SARIMA) models and autoregressive integrated moving average (ARIMA) forecasting models to predict the variables’ trends for 2030. One significant contribution of this study is the development of a methodology to assess Brazil’s energy efficiency, considering environmental and economic factors to formulate results. These results can help create policies to make SDG7 a reality and advance Brazil’s energy strategies. According to the study results, the annual energy consumption rate is projected to increase by an average of 2.1% by 2030, which is accompanied by a trend of GDP growth. By utilizing existing technologies in the country, it is possible to reduce electricity consumption costs by an average of 30.58% while still maintaining the same GDP value. This demonstrates that sustainable development and adopting alternatives to minimize the increase in energy consumption can substantially impact Brazil’s energy sector, improving process efficiency and the profitability of the Brazilian industry.

Edukasi Penerapan Prinsip Akuntansi Lingkungan Melalui Efisiensi Energi dan Pengelolaan Limbah Pada UMKM Tambakrejo Semarang

Management understanding and knowledge of environmental accounting, especially related to waste management and energy efficiency, is very important to achieve business sustainability, including for Micro, Small, and Medium Enterprises (MSMEs). The Tambakrejo MSME group often lacks understanding of milkfish waste management and faces increasing energy operational costs, especially for refrigerators. Lack of environmental awareness causes milkfish waste to pollute the environment and hinder water drains. Environmental accounting integrates environmental aspects in accounting practices to measure, report, and manage environmental impacts. This Community Service educates about waste management strategies and energy efficiency based on environmental accounting principles. The results of Community Service have a positive impact, the enthusiasm of MSME actors is very high, this is evidenced through pre and post testing measuring tools for MSMEs with the results of the increasing level of public understanding of environmental awareness and energy saving in MSMEs, as well as teaching environmental cost management methods through identification, recognition, measurement, reporting, and integration of environmental aspects in the accounting system.

Taxonomy of optimization algorithms combined with CNN for optimal placement of virtual machines within physical machines in data centers

Energy management in datacenters is a major challenge today due to the environmental and economic impact of increasing energy consumption. Efficient placement of virtual machines in physical machines within modern datacenters is crucial for their effective management. In this context, five algorithms named CNN-GA, CNN-greedy, CNN-ABC, CNN-ACO and CNN-PSO, have been developed to minimize hosts’ power consumption and ensure service quality with relatively low response times. We propose a comparative approach between the developed algorithms and other existing methods for virtual machine placement. The algorithms use optimization algorithms combined with Convolutional Neural Networks to build predictive models of virtual machine placement. The models were evaluated based on their accuracy and complexity to select the optimal solution. The necessary data is collected using the CloudSim Plus simulator, and the prediction results were used to allocate virtual machines according to the predictions of the models. The main objective of this research is to optimize the management of Information Technology resources within datacenters. This is achieved by seeking a virtual machine placement policy that minimizes hosts’ power consumption and ensures an appropriate level of service for users' needs. It considers the imperatives of sustainability, performance, and availability by reducing energy consumption and response times. We studied six scenarios under specific constraints to determine the best model for virtual machines’ placement. This approach aims to address current challenges in energy management and operational efficiency.

An efficient and resilient energy management strategy for hybrid microgrids inspired by the honey badger's behavior

This manuscript introduces a highly efficient hybrid renewable energy system (HRES) that combines photovoltaic (PV) panels and wind turbines (WTs) as primary power sources, supplemented by three backup systems: batteries, hydrogen energy storage systems (HESSs), and supercapacitors (SCs). This system employs a multi-objective optimization algorithm to dynamically identify the best energy management system (EMS). The performance of the proposed EMS across different operational scenarios is assessed using seven distinct algorithms to identify the best solution, particularly emphasizing two main objective functions: operating cost and loss of power supply probability (LPSP). Furthermore, the study rigorously evaluates the performance of these algorithms against nine benchmarks to determine the most appropriate algorithm for the HRES under consideration. In the 1st scenario, power demand is met using both primary and backup energy sources. The honey badger algorithm (HBA) proved its cost-effectiveness and efficiency by achieving the lowest overall operating cost and the highest efficiency of 95.893 %. Additionally, its computation time was notably faster, being 70 s–488 s quicker than the other algorithms. The 2nd scenario arises when power demand exceeds the available generation capacity because of the charging limits of all energy storage systems (ESSs), which prevents them from providing power to the load. The HBA achieved the lowest cost and matched the highest efficiency score of 96.691 %. Furthermore, HBA provided the quickest optimization, completing the process 400 s–450 s faster than the other techniques. In the 3rd scenario, the generated power surpasses the current load demand, and the ESS components are fully charged therefore, the proposed EMS efficiently directs excess power to a dummy load, preventing overcharging of storage components and enhancing grid stability. The HBA consistently exceeded the performance of other algorithms, achieving the lowest average cost, maintaining the highest efficiency of 95.305 %, and solving the optimization problem 300 s–500 s faster than the alternatives. Consequently, these results highlight HBA's flexibility and effectiveness, positioning it as a reliable choice for optimizing energy systems across various operational scenarios.

Efficient and Lightweight Model-Predictive IoT Energy Management

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem, and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Energy efficient resource management in data centers using imitation-based optimization

Cloud computing is the paradigm for delivering streaming content, office applications, software functions, computing power, storage, and more as services over the Internet. It offers elasticity and scalability to the service consumer and profit to the provider. The success of such a paradigm has resulted in a constant increase in the providers’ infrastructure, most notably data centers. Data centers are energy-intensive installations that require power for the operation of the hardware and networking devices and their cooling. To serve cloud computing needs, the data center organizes work as virtual machines placed on physical servers. The policy chosen for the placement of virtual machines over servers is critical for managing the data center resources, and the variability of workloads needs to be considered. Inefficient placement leads to resource waste, excessive power consumption, and increased communication costs. In the present work, we address the virtual machine placement problem and propose an Imitation-Based Optimization (IBO) method inspired by human imitation for dynamic placement. To understand the implications of the proposed approach, we present a comparative analysis with state-of-the-art methods. The results show that, with the proposed IBO, the energy consumption decreases at an average of 7%, 10%, 11%, 28%, 17%, and 35% compared to Hybrid meta-heuristic, Extended particle swarm optimization, particle swarm optimization, Genetic Algorithm, Integer Linear Programming, and Hybrid Best-Fit, respectively. With growing workloads, the proposed approach can achieve monthly cost savings of €201.4 euro and CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document} Savings of 460.92 lbs CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document}/month.

Efficient Method for Photovoltaic Power Generation Forecasting Based on State Space Modeling and BiTCN

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.

Assessing the influence of environmental variables on energy efficiency changes in the provision of drinking water services

Within the context of the water-energy nexus in drinking water provision, it is crucial to evaluate temporal shifts in energy efficiency. Utilizing the Stochastic Nonparametric Envelopment of Data (StoNED) approach, this research estimates longitudinal variations in the energy efficiency of drinking water services integrating multiple environmental variables. The analysis, conducted in England and Wales from 2008 to 2020, indicates that the source and quality of raw water, as well as population density, influenced the energy performance of water utilities. Quantitative findings evidenced marked disparity in energy efficiency among different companies, with an average efficiency of 0.617 and a range from 0.437 to 0.944. The dynamic assessment indicates an average annual enhancement of 1.1% in the sector, predominantly propelled by technical advancements (0.9%) rather than by improvements in efficiency (0.2%). This study emphasizes the critical need for bespoke policies and incentives to boost energy efficiency and support sustainable urban water management.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING DROOP CONTROL TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using the droop control technique. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing an efficient energy management system for a small-scale hybrid wind-solar-battery-based microgrid to extract maximum profit from electricity generation. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review

In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation’s unpredictable nature, deterministic point forecast methods struggle to accurately assess the uncertainties associated with PV generation. This paper presents a detailed structured review of the state-of-the-art concerning Probabilistic Solar Power Forecasting (PSPF), which covers forecasting methods, model comparison, forecasting horizon and quantification metrics. Our review methodology leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to systematically identify primary data sources, focusing on keywords such as probabilistic forecasting, Deep Learning (DL), and Machine learning (ML). Through an extensive and rigorous search of renowned databases such as SCOPUS and Web of Science (WoS), we identified 36 relevant studies (n=36). Consequently, expert scholars decided to develop three themes: (1) Conventional PSPF, (2) PSPF utilizing ML, and (3) PSPF using DL. Probabilistic forecasting is an invaluable tool concerning power systems, especially regarding the rising proportion of renewable energy sources in the energy mix. We tackle the inherent uncertainty of renewable generation, maintain grid stability, and promote efficient energy management and planning. In the end, this research contributes to the development of a power system that is more resilient, reliable, and sustainable.

Methods of reducing fuel consumption by seagoing vessels in order to reduce emissions of harmful substances into the atmosphere

The aim of the paper is to draw attention to the emissions of toxic compounds into the atmosphere problem by seagoing ships. Annex VI of the MARPOL Convention, which came into force in 2005, concern the prevention of air pollution from ships, forced shipowners to use solutions aimed at limiting the emission of these harmful substances into the atmosphere. One of the instruments enabling the implementation of these guidelines is the design energy efficiency index EEDI, introduced obligatorily for designed and newly built ships. In turn from 2013, the regulations also oblige shipowners to maintain an effective energy management plan during ship's operation, SEEMP (Ship Energy Efficiency Management Plan). The paper presents factors that influence the reduction of fuel consumption by ships, leading to a reduction in emissions of harmful substances into the atmosphere and thus achieving the required EEDI and other indicators. These are factors related to the ship's hull, the main propulsion engine and the propeller-rudder system. The influence of each of these factors on the level of fuel consumption by the ship was indicated. The work highlights the methods of reducing fuel consumption by seagoing ships aimed at reducing emissions of toxic compounds into the atmosphere, which are currently the most popular and effective, and relate mainly to the hull and main propulsion engines.

Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

Analyzing green entrepreneurial orientation drivers: Insights from the Indian manufacturing industry

ABSTRACT Business activities often lead to various environmental issues, like deforestation, waste, and pollution. Hence, integrating environmental concerns into manufacturing operations is essential for ensuring long-term sustainability. In this direction, green entrepreneurial orientation (GEO) represents an internal strategy that integrates environmental concerns into business operations. While much of the discussion around GEO has centered on the manufacturing sector in general, the unique dynamics of the textile industry have often been overlooked. This highlights the need for a more targeted exploration of GEO drivers specific to the textile sector. An exploratory approach has been used in the study, where in the first stage drivers are identified through a literature review strategy, while the second stage involves analysis of the company sustainability reports to validate the drivers. The results indicate that managerial environment concerns, technical orientation, big data analytical capabilities, environmental collaboration, and institutional pressure are the primary drivers that must be operationalized by firms to implement GEO. Furthermore, textile firms are concentrating on five core areas that align with GEO principles: energy efficiency, renewable energy, water and effluents management, waste management, and awareness programs. The study provides implications for managers and policy makers to prioritize the integration of environmental concerns into strategic decision making, foster collaborations, invest in data-driven technologies, and strengthen regulatory frameworks to encourage the adoption of green entrepreneurial practices.

Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies

This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system.

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

A fully decentralized demand response and prosumer peer-to-peer trading for secure and efficient energy management of community microgrid

A novel demand response program combines the advantages of price-based demand response under the time-of-use pricing and the incentive-driven demand reduction bidding strategy to manage energy demand, and maximize the net present value of both participants and the community-microgrid. It also facilitates the seller prosumers to optimally sell the surplus power in a fully decentralized peer-to-peer power trading manner. A novel mathematical demand reduction bidding model is proposed to minimize the community-microgrid cost by the prioritized selection of participants for demand reduction. This study proposed a novel decentralized three non-cooperative dynamic games strategy, incorporating Nash equilibrium approaches to achieve the optimum schedules of appliance, buying, and power trading agents to maximize the net present value of both prosumers and the community-microgrid while preserving participants’ privacy and control network congestion. The priority-based optimal selection of seller and buyer prosumers according to their utility and pricing function in the peer-to-peer power trading scheme is accomplished using a proposed color-based segmented decentralized bilateral power trading to maximize the net present value. The numerical and simulation results demonstrate the feasibility and effectiveness of the proposed strategies and increase the net present value of prosumers up to 42.4 % and community-microgrid up to 34.79 %.

Testing Time Optimization Method for IEEE 802.15.4z Ultra-wideband Integrated Circuits

IEEE 802.15.4z-compliant ultra-wideband (UWB) devices are becoming ever more popular in contemporary radio engineering systems. Such systems are capable of precisely measuring distances (with their accuracy expressed in centimeters), are immune to interference, offer low latency and transmit data in an energy-efficient manner. Widespread adoption of UWB technology has triggered significant demand for testing integrated circuits these systems rely on, prompting the development of new testing methods to meet the ever increasing requirements in terms of testing speed and reliability. The same applies to sensitivity tests, in the course of which up to 2000 different packets may be received. The process of generating and analyzing such a large number of packets is time consuming. Furthermore, if multiple devices need to be tested simultaneously, the duration of the test will be multiplied accordingly. In such a context, the article investigates the lead time required to generate 2000 UWB packets using conventional methods and proposes a novel approach to significantly reduce packet generation time and improve testing efficiency.

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The national system of energy saving management in budget sector organizations is presented through the prism of the implementation of targeted directions for increasing the level of energy efficiency by the program-target method, the purpose of which is the concentration of resources of business entities and the construction of a unified energy efficiency management system and the coordination of efforts of both state and regional authorities, local self-government, institutions,etc. An important role is given to the components of the program-target method as a mechanism of state management of a unified system of state and regional authorities and local self-government bodies, institutions, etc., which, in particular, will ensure the coordination of energy components and the formation of an acceptable price for educational services. According to the results of the research, the need for the formation of an energy-efficient policy in each institution of higher education, despite its regional location, based on a comprehensive approach from the standpoint of a single energy space, has been actualized. The author considers the management of energy saving projects of the budget sector organization in relation to their energy security system, i.e. the management system of a budget institution from the standpoint of a single energy space, which provides an effective, reliable and ecologically safe environment, which is minimally affected by internal and external factors of energy saving, and is the guarantor of the quality of services, in particular energy services, provided to the population by public sector facilities. The main factors of energy efficiency of budget sector organizations, the procedure for implementing the energy saving and energy efficiency improvement program, the main factors of managing energy efficient projects in the budget sector, analytical tools for modeling their evaluation system and the stages of developing a scenario of an innovative model of their management are presented. The proposed technologies and tools can be used to build an innovative management model of energy saving projects of budget organizations, in particular, using the pyramid-aggregate modeling method.