Energy Consumption Management Research Articles

A comparison of the cooling and heating performance of two passive systems of central courtyards and atriums at an elementary school in Yazd City

Energy consumption management in building designs, which designers practice today, has been helping reduce operational and implementation costs. These design techniques can be utilized in the long run as well. As a traditional element of the Iranian architecture, central courtyards have been playing a major role in reducing energy consumption and providing comfort to the occupants. Atriums, as transparent spaces, also help reduce energy consumption through sunlight. In addition, they provide residents with a sense of satisfaction through thermal comfort and lighting during the day in buildings. The present research aims to compare the performance of central courtyards and atriums as well as their combination to determine their effects on energy consumption reduction in an educational building. The case study proposed for this research was Yazd City, Iran. This research designed the school building in two different states and simulated it in DesignBuilder software. The building had only one central courtyard in the first state, while the central courtyard was converted into an atrium in the second one. Simulation process results showed that the conversion of the central courtyard into an atrium helped reduce heating and lighting energy consumption by 30 and 51%, respectively. In addition, the amount of cooling energy consumption in the case of the atrium with no canopy increased by about 32%, which could be reduced by 27% via using shading.

Thermal Management and Energy Consumption in Air, Liquid, and Free Cooling Systems for Data Centers: A Review

The thermal management and reduction of energy consumption in cooling systems have become major trends with the continued growth of high heat dissipation data centers and the challenging energy situation. However, the existing studies have been limited to studying the influences of individual factors on energy saving and thermal management and have not been systematically summarized. Thus, this paper reviews the key factors in achieving thermal management and reducing energy consumption in each cooling system, the corresponding research, and optimization methods. To achieve these goals, in this paper, literature surveys on data center cooling systems are investigated. For data center air cooling, thermal management is mainly related to the uniform distribution of hot and cold air. Adjusting the porosity of perforated tiles can reduce energy consumption. For liquid cooling and free cooling systems, climate conditions, cooling system structural design, coolant type, and flow rate are key factors in achieving thermal management and reducing energy consumption. This paper provides the power usage effectiveness (PUE) values of the cooling systems in some cases. A summary of the key factors can provide directions for research on thermal management and energy reduction, and a summary of previous research can provide a basis for future optimization.

RESEARCH OF GLOBAL ENERGY TRENDS AIMED AT THE DEVELOPMENT OF ACTIVE ENERGY COMPLEXES

The article is devoted to the study of modern trends in the development of the global fuel and energy complex with the identification of features and key areas that determine the nearest policy in the field of energy conservation and energy efficiency improvement. The materials analyze the dynamics of changes in demand for consumption of various types of energy resources, changes in the structure of the world energy balance, emphasizing the continuing low level of energy efficiency in many countries of the world. Taking into account the significant amount of investments in renewable energy development technologies, the high growth rates of installed capacity of power plants operating on renewable energy sources over the past decade, as well as the high rates of global growth in demand for electricity consumption, one of the key areas in improving the efficiency of the global fuel and energy complex will be the use of distributed generation, including among those operating on the basis of renewable energy sources. Integration of technologies of small distributed generation with modern technologies of energy consumption management at the level of final consumption, such as management, demand for electricity and natural gas consumption and the formation of active energy complexes on the basis of industrial enterprises, will be full-fledged participants in the circulation of energy resources in Unified energy systems and Unified gas supply systems of the future.

Design and key technology of the energy consumption management system for the liquid cooling data center

AbstractIn view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. Particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

Effect of biochar from municipal solid waste on mechanical and freeze–thaw properties of concrete

Global municipal solid waste (MSW) generation and cement manufacture pose catastrophic threats to the environment. Although positive impacts have been demonstrated for biochar originating from single biomass in concrete, limited studies focused on the biochar from the pyrolysis of municipal solid waste, and the freeze–thaw resistance of biochar concrete still remains unknown. This paper explored the feasibility of biochar derived from MSW at 600℃ to replace 1%-30% cement by weight in concrete. Properties of water absorption, setting time, mechanical properties and freeze–thaw resistance for the mixture systems were studied with the inspections of Scanning electron microscope (SEM), Fourier transforms infrared spectrometer (FT-IR) and X-ray diffraction (XRD). The results suggested that biochar replacement reduced the setting time of the binder system with an increase in water requirement of normal consistency. The compressive strength of concrete was increased by 9.2% with less than 5% of cement replaced. Biochar markedly improved the flexural and splitting tensile strength of concrete and thus played a beneficial role in toughness by 20% at 10% cement replaced. Concrete with a tiny proportion of biochar exhibited the weakest freeze–thaw resistance than control and 10% replacement. Biochar replacement promoted the formation of calcium carbonate. Biochar replacement for partial cement in concrete provides a sustainable option for municipal solid waste management and building energy consumption reduction.

Predictive Energy Management in Internet of Things: Optimization of Smart Buildings for Energy Efficiency

As energy efficiency and sustainability become paramount in the face of growing urbanization and environmental concerns, predictive energy management in smart buildings has emerged as a promising avenue for mitigating energy consumption and optimizing resource utilization. In this paper, we investigate the application of advanced machine learning techniques, particularly a multi-layer Long Short-Term Memory (LSTM) model, within the framework of the Internet of Things (IoT), to predict and manage energy consumption. We rigorously evaluate our approach against a suite of machine learning baselines, including Linear Regression, Random Forest, Support Vector Machine, and Gradient Boosting, utilizing a comprehensive dataset encompassing power consumption data from smart home appliances and associated weather variables. Our experimental results demonstrate the superior predictive capabilities of the LSTM model, showcasing its ability to outperform traditional machine learning baselines across various metrics, including Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings underscore the potential of deep learning models in capturing intricate temporal dependencies within energy consumption data, contributing to improved energy efficiency, cost savings, and environmental sustainability in smart building environments. The integration of predictive energy management models into IoT-enabled smart buildings holds the promise of a more intelligent and sustainable future in urban development and resource management.

An overview of smart grid smart meters with fault detection

Smart meters (SM) arrangement play an essential role in Smart Grid (SG), serving as advanced energy meters crucial for monitoring and managing energy consumption. This paper provides an in-depth exploration of the significance, functions, and technological aspects of smart meters within the context of the SG. Smart meters acquire data from endusers' load devices, offering a comprehensive measurement of consumers' energy consumption. The obtained information is then communicated to utility companies and system operators, facilitating efficient energy management. The integration of control devices and a number of sensors, supported by a dedicated communication infrastructure, enhances the reliability and functionality of smart meters. The paper summarizes hardware model key facets and functions of smart meters, offering a comprehensive view of their capabilities.

THE CONCEPT OF SMART GRID: TECHNOLOGICAL, ORGANIZATIONAL AND ECONOMIC ASPECTS OF THE DEVELOPMENT OF THE ENERGY SECTOR

Purpose. The aim of the article is to substantiate the concept of Smart Grid application from the point of view of technological, organizational and economic aspects of development in the energy sector, outlining the features of their use in modern conditions and in the future. Methodology of research. The methodological basis of the research is the dialectical method of scientific knowledge. General scientific and special methods were used in the research process, in particular analysis and synthesis, dialectical – to determine the initial conditions and content of the Smart Grid; analytical – when processing literary and Internet sources; causal – to determine the dynamics of Smart Grid technology development in the world; abstract and logical – for outlining the main directions of operation of Smart Grid technologies in the field of energy, forming conclusions and proposals. Findings. Current and prospective Smart Grid technologies in the energy industry are summarized. The need for intellectualization of the electric power industry through the formation of a digital electric power network is substantiated. Measures to create a unified technical and informational infrastructure for electricity producers and consumers thanks to the Smart Grid concept have been determined. An assessment of the dynamics of investment in Smart Grid technologies in the world was carried out, the volume of the world market of Smart Grid technologies was determined, and the income of the world market of electricity supply and management was analysed. Originality. Measures to increase the introduction of Smart Grid in the energy sector are substantiated, which will make it possible to strengthen Ukraine's energy independence and contribute to the introduction of energy-saving technologies. The main vectors of the development of the electric power industry using the Smart Grid concept are proposed: design and use of modernized, new and breakthrough (critical) technologies; development of new energy management systems based on the principles of information interaction of energy facilities; involvement of individual and collective consumers in energy consumption management. Practical value. The substantiated research results can be used for the formation of innovative Smart Grid technologies in the field of energy security of Ukraine. Key words: Smart Grid, intelligent network, energy, energy security, energy supply, renewable energy sources.

Effective energy management tools: inventory management and monitoring of energy consumption by personnel

This article introduces an innovative management system for retail businesses, based on modern computer vision and data analytics technologies. The primary focus is on effective energy management: optimizing inventory control and monitoring employee energy consumption. The system is designed to address several key challenges faced by stores and trading companies. It provides a comprehensive solution for employee monitoring, utilizing computer vision for automated attendance tracking and establishing a detailed absence database. This streamlines supervisory responsibilities and ensures fair payroll distribution, eliminating unjustified payouts for absenteeism. Furthermore, the system revolutionizes inventory management by offering accurate sales statistics through automated data entry and in-depth analytics. It identifies popular items and forecasts future needs, optimizing procurement decisions and preventing losses due to stock shortages.

Study on Building Energy Saving Effect and Thermal Response of Roof Greening in Extremely Hot Weather

As the main living space and carrier of the population, cities and buildings have become more seriously affected by changes in global environment in recent years. The deteriorating living environment has brought about the quality of survival of the population, the government’s administrative management, and the sustainable development of the environment. This article has found that green roofs on urban environmental management and building energy consumption have found that green roofs can have an important impact on urban environmental management. In extreme weather events, green roof has a maximum cooling effect of 26℃ in the daytime, and its warming effect at night has been suppressed to a certain extent; the heat preservation time of the green roof can be up to 17 hours after the superposition of precipitation weather incidents, and its heat preservation effect is positively related to the temperature in the daytime. The green roof can reduce the energy consumption of the building by 106W/ ㎡/d in the extreme weather incident, while its energy -saving effect is reduced to 45W/m2/d after superimposing the precipitation meteorological incident.

AI-POWERED PRECISION AGRICULTURE: RESHAPING FARMING FOR EFFICIENCY, SUSTAINABILITY, AND GLOBAL IMPACT

This article delves into the transformative effects of Artificial Intelligence (AI) on precision agriculture, also known as AgriTech or AgTech. The convergence of AI and farming practices reshapes the industry by leveraging machine learning, data analytics, and automation to optimize resource allocation and decision-making. Precision agriculture maximizes crop yields while minimizing waste through real-time data from AI technologies such as remote sensing, satellite imagery, and IoT sensors. AI-driven insights enable precise recommendations for irrigation, fertilization, and pest control, enhancing productivity and reducing environmental impact. Moreover, AI enhances crop health assessment, accelerates genetic research, and fosters climate-resilient crops. The article explores AI's potential in predictive analytics, genetic editing, global food security, and sustainable practices. Automation in agriculture tasks via AI-driven robotics and machinery elevates productivity. While AI offers positive environmental impact through resource optimization and energy efficiency, concerns about energy consumption and hardware management warrant attention. A balanced approach to AI's development, embracing energy-efficient algorithms and renewable energy integration, ensures its positive contribution to sustainable practices. The article concludes by discussing potential research directions and the complex interplay of AI's benefits and environmental concerns.

Research on power quality assessment based on ubiquitous power IoT

Abstract With the rapid development of the Internet in this era, the traditional power grid is no longer able to meet the growing demand of the people for electricity. In order to realize a new generation of power system with comprehensive sensing, reliable transmission, intelligent processing and interconnection, the State Grid proposes the strategic goal of developing ubiquitous power IoT. Firstly, the concept and basic architecture of ubiquitous power IoT are elaborated, and then the main key technologies of the architecture of power energy consumption monitoring and management system are explained. The power quality parameters are measured and calculated, and the G1 method, which is computationally small and does not require consistency testing, is used to determine the subjective weights in the power quality evaluation process, which can truly reflect the power quality level of power grids in different regions. The information entropy method is used to determine the objective weights in the process of power quality evaluation to solve the error of artificially determined weights, and the combination of subjective weights and objective weights can more truly reflect the influence of each index on the comprehensive level of power quality. The results of the study show that PV is rated between 2 and 3 good when grid-connected and off-grid, and wind turbines are rated between 3 and 4 medium when grid-connected and off-grid. The results of the integrated assessment levels are in line with the actual situation and can meet the growing demand for electricity.

Research on the Demand and Business Prospect of Informatization Based Digital Energy Use Service in Parks

With the in-depth development of digital technologies such as informatization, networking, big data, intelligence and intelligence, the digital management of energy consumption in the park has become the development direction of intelligent parks in the future. This paper analyzes the energy consumption and digital service demand of enterprises in the park, analyzes and prospects the business prospect of energy consumption digital service in the park, and puts forward suggestions from the perspective of market operation mode and technology, in order to provide reference for the development of energy consumption digital service in the park in the future.

IOT-Based Energy Monitoring System for Energy Conservation

The aim of this project is to develop an Internet of Things (IoT) based Energy Monitoring System to reduce energy waste and cutting down energy cost. Therefore, it needs a system which provide efficient energy consumption management. For the starting of designing an IoT-based Energy Monitoring System, different sensor and calculated AC measurement methods were studied. The development of this system is to integrate each of the sensor, energy measuring device and IoT system into one complete module. The concept of Wireless Sensor Network (WSN) was implemented in this project. The WSN obtained data information from sensor and send them to the cloud through the IoT network for cloud storage of the ThingSpeak platform. The system contains two input signal which is voltage and current. The device that transfers the data or information from the energy monitoring device to the cloud storage is the ESP8266 Wi-Fi module. The output is the energy consumption that has been used according to the real-time data measurement. Lastly, the data that is transmitted to the cloud can be monitored through the mobile application ThingView. In conclusion, this system is necessary because it can control and manage energy consumption to avoid wastage and promote energy conservation.

Optimizing cloud resource scheduling for enhanced energy efficiency

Energy consumption in cloud computing plays a vital role in operating costs for both the service provider and the cloud user. The cloud is scalable and can provide access as per demand. Due to this, resource access requests are increasing and submitted to the server. To manage all requests, scheduling is the solution to assign requests with the quality of service. To avoid high operating costs, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposed. The total resource utilization of each resource has been calculated, and energy is optimized through the antlion optimization algorithm to avoid high power consumption. The resource is identified with its best utilization value and assigned to submitted workloads on a priority basis. The experimental results of the proposed work are analyzed with existing autonomic frameworks, and it is found that the proposed work performs exceptionally well. In addition to addressing the increasing resource access requests, the proposed energy-aware resource scheduling in cloud computing also focuses on optimizing overall system performance. The antlion optimization algorithm is employed to efficiently manage energy consumption, ensuring that power usage is minimized without compromising the quality of service. The algorithm calculates the total resource utilization for each component in the cloud infrastructure, allowing for a thorough understanding of system dynamics. The paper optimizes cloud computing by prioritizing efficient and capacity-based resource assignment, improving responsiveness, user experience, and reducing costs. The approach also promotes environmental sustainability. Experimental results validate its effectiveness in energy efficiency and system responsiveness, contributing to advanced resource management in cloud computing for sustainable and cost-effective services.

Пинчанализ – как инструмент повышения энергетической эффективности НПЗ

The article is devoted to the problem of reserves and opportunities for further improvement of oil refineries in the field of energy efficiency and energy conservation. The results of rational use of energy resources and their savings, as well as improvement of the process of energy consumption management in the implementation of all typesof the company’s activities are shown on the example of the production activities of PJSC «NK «Rosneft».The assessment of the existing methods and approaches used to analyze the energy efficiency of technological processes and aggregates in terms of their functionality is given. From the considered set of methods, the Pinch method is singled out as the most promising for a comprehensive analysis of production in order to optimizethe consumption of various energy resources when designing new enterprises or production units, as well as modernization of production facilities. It is presented the methodology of using Pinch analysis to improvethe operational efficiency of a number of specific refinery facilities. In conclusion economic results are shown and promising directions are outlined for the development of energy saving works using the new method.

WLTC measuring driving cycle (power reserve measurement procedure for hybrids and electric vehicles)

Problem. The most effective energy management strategies for hybrid vehicles and electric vehicles are optimization-based strategies. These strategies require prior knowledge of the driving cycle, which is not easy to predict. Goal. The goal is to combine the Worldwide harmonized light vehicles test cycle (WLTC) with short trips on small sections with real traffic levels to predict the energy and fuel consumption of hybrid vehicles and electric vehicles. Methodology. Research methods are experimental and mathematical. First of all, eight characteristic parameters are extracted from real speed profiles used on urban road sections in the city of Kharkiv under various road conditions, as well as on short WLTC trips. The minimum distance algorithm is used to compare parameters and determine three traffic levels (heavy, medium, and low traffic) for short WLTC trips. Thus, for each route determined using Google Maps, the energy and fuel consumption of hybrid vehicles and electric vehicles are estimated using short trips by the WLTC, adjusted for distances and traffic levels. In addition, a numerical model of the vehicle was implemented. It was used to test the accuracy of predicting fuel and energy consumption in accordance with the proposed methodology. Originality. For the methodology using only GM information is required as input data; no other device or software is required. This aspect makes the methodology extremely economical. Then, the algorithm regulating traffic levels shown by GM is unique and valid in all urban centers. This aspect makes the methodology universal. WLTC takes into account the driving styles of drivers around the world, so the methodology can be applied to any car driver. Prediction accuracy can be increased by taking into account other input information, such as the distribution of traffic light signals or the driver's typical gear shifting style. Results. The results are promising, as the average absolute percentage error between experimental driving cycles and projected ones is 3.89 % for fuel consumption, increasing to 6.80 % for energy consumption. Practical value. The possibility of energy forecasting and fuel consumption for a hybrid vehicle and an electric vehicle makes it possible to develop energy consumption management systems for HEVs that can manage the energy reserve to ensure full travel by electric traction in limited traffic zone (LTZ) or minimize local air pollution; increase the service life of energy reserves (usually batteries) by maintenance costs and disposal problems reducing; optimize the transmission-use efficiency due to fuel consumption and pollutants emissions reduction.

Design and Implementation of a Cloud-IoT-Based Home Energy Management System

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications.

Research on energy‐saving virtual machine migration algorithm for green data center

AbstractThe cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service.

A novel methodology for the estimation of failure behavior of "fair" smart meters and analysis of their circular economy chain.

Electric power utilities are striving to address critical challenges such as energy consumption, material recovery, e-waste, procurement, and supplier working conditions. Currently, they are converting their old infrastructure into smart grids. The installation of smart meters is a key step in this process. Since a smart meter is an intelligent and modern measuring device that includes computer-aided measurements while also allowing intelligent management and determination of residential and industrial users' energy consumption and supply, their deployment in smart grids is of major importance. In this study, considering that the estimated number of smart meter units will be 188.12 million units by 2025, five different types of smart meters are used to estimate their failure behavior. We adopted the probability of smart meters' survival, considering the number of the components included in their PCBs, while the influence of their components follows an exponential distribution for a given lifetime. The meaning of the "fair" smart meter is introduced to solve critical concerns such as energy use, material consumption, e-waste, supplier sourcing, and labor conditions. To achieve the above targets, a circular economy chain analysis is implemented by dismantling the existing smart meters, classifying their materials into five primary groups and weighing them to obtain average values. Moreover, we calculate the average cost of the components using their equivalent market value as provided by stock markets to get the average weight of each component in terms of material cost. Finally, we introduce the "remanufacturing index" and the "reusing index" indices as procedure metrics to further quantify the circular economy chain results. The results show that the percentage of the reusing procedure in the "fair" smart meter circular economy chain is greater than the corresponding percentage of the remanufacturing procedure, while the percentage of the recycling procedure is increased as the recycling cost per unit, is also increased.