Analysis Of Energy Consumption Research Articles

Improving Sustainable Energy Distribution in the USA: Leveraging the Theory of Constraints’ Thinking Process Tools

This study explores the improvement of sustainable energy distribution in the USA through the application of the Theory of Constraints’ Thinking Process Tools. The research adopts a mixed-methods approach, combining quantitative analysis of energy production, consumption, and transmission data with qualitative analysis of stakeholder perspectives and regulatory frameworks. The findings reveal key challenges, including infrastructure bottlenecks, regulatory barriers, and coordination issues among stakeholders. Through the application of the Theory of Constraints’ tools, targeted strategies are developed to address these constraints and enhance the efficiency, reliability, and sustainability of the energy distribution system. Recommendations are provided to invest in infrastructure upgrades, streamline regulatory processes, foster stakeholder collaboration, prioritize research and innovation, and promote public awareness and engagement. By implementing these recommendations, stakeholders can work together to overcome existing challenges and accelerate the transition towards a more sustainable energy future in the USA.

Analysis of Aerosol Removal Efficiency and Electrical Energy Consumption in the Use of Air Conditioner and Air Purifier in a Room

Analysis of Aerosol Removal Efficiency and Electrical Energy Consumption in the Use of Air Conditioner and Air Purifier in a Room

Analysis and Prediction of Energy Consumption in Neural Networks Based on Machine Learning

Analysis and Prediction of Energy Consumption in Neural Networks Based on Machine Learning

Is Fiber-To-The-Room (FTTR) Green? ̶ Modeling and Analysis of Power and Energy Consumption

Is Fiber-To-The-Room (FTTR) Green? ̶ Modeling and Analysis of Power and Energy Consumption

ОБОСНОВАНИЕ ЭНЕРГОСБЕРЕГАЮЩЕЙ ФУНКЦИОНАЛЬНОЙ СХЕМЫ АККУМУЛЯЦИОННОЙ УСТАНОВКИ ДЛЯ НАГРЕВА ВОДЫ НА ФЕРМАХ КРС

Analysis of electric energy consumption on cattle farms has shown that hot water supply is one of the energy-intensive technological processes on such farms. In order to save energy resources when heating water, a functional scheme of an accumulator installation has been developed that allows heating water for watering cows and providing technological needs. The standard water temperature for drinking is 12-15 degrees Celsius, for technological needs - 55-65 degrees Celsius. (Research purpose) The research purpose is substantiating and developing a functional scheme of an energy-saving installation for heating water using solar radiation for technological needs and thermal energy of ventilation air removed from the premises for watering cows on cattle farms. (Materials and methods) Applied patent search on the websites of Rospatent and Yandex.Patents. The analysis of analytical calculation dependencies was performed to determine the main technical characteristics of water heating installations for animal watering and technological purposes. (Results and discussion) A functional scheme of the hot water supply installation was proposed, including a water heater for technological needs using solar radiation energy and a water heater for animal watering using a thermoelectric heat pump and the heat of the ventilation air removed from the farm premises. (Conclusions) Developed a functional scheme of an accumulative water heating unit for watering cows and technological needs on cattle farms. It has been shown that the proposed scheme allows using the heat of the air removed from the premises and the renewable heat of solar radiation as alternative sources to traditional methods of heating water. The possibility of using low-potential thermal energy of the removed ventilation air for hot water supply of the animal watering system using a thermoelectric heat pump and heat pipes, as well as a solar collector for heating water for the technological needs of the farm, was investigated.

Energy Consumption Characteristics Study of Surface Meteorological Observation Station in North and South China

Surface meteorological observation is one of the most important sources of primary data for the meteorological sector and are crucial to the accuracy of weather forecasting. China’s meteorological authorities have built a dense network of surface observation stations, which require a considerable amount of electrical energy to keep the network running normally. There has been a lack of analytical studies dedicated to the energy consumption of surface meteorological observation stations domestically and internationally. This paper selects measured energy consumption data of 4 surface meteorological observation stations in two typical provinces (autonomous regions) in the north and south China over the past two years, and proposes, for the first time, to use the average power of the surface meteorological observation station, adopts the electrical energy consumption calculation method, carries out energy consumption calculation and analysis by month, sub-region and season, derives the characteristics of energy consumption changes of the surface meteorological observation station, and proposes energy saving and consumption reduction measures.

ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment

ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment

A comparative analysis of energy consumption in conventional and electric vehicles

A comparative analysis of energy consumption in conventional and electric vehicles

Implementation of smart building management systems for residential complex “PANORAMA”

The European Union (EU) and Ukraine, which is following a precise European integration course, have a priority to improve energy efficiency in order to prevent negative environmental impact and achieve climate neutrality. It is known that more than 40% of the energy produced is accounted for by energy consumption in existing buildings. Analysis of energy consumption reveals a high percentage of energy losses due to poor management, which necessitates the development and implementation of systems and tools for improved energy management. Therefore, the development of energy efficient building management systems using modern engineering and technical solutions to control and optimise energy consumption is a relevant and promising way to solve the issue of energy resources saving, increase energy efficiency and enhance energy independence. Within the framework of the international research grant “A novel decentralized edge-enabled PREsCriptivE and ProacTive framework for increased energy efficiency and well-being in residential buildings” – PRECEPT under the Horizon 2020 programme, smart building management system was developed and implemented at the residential complex “PANORAMA” in Dnipro city, Ukraine.

Green ICT Methodology for Energy Consumption Calculation in ICT Architecture Components

As the usage of information and communication technology (ICT) solution have been exploded in the last decades, the research community exhibits a deficiency in terms of methodologies for accurately quantifying the energy consumption of individual ICT components. This paper presents a novel methodology aimed at addressing the existing research gap by offering a comprehensive analysis of energy consumption ranges for ICT components, measured in Watt. The methodology being proposed involves the classification of ICT components into distinct classes, namely sensors, gateways, servers-platforms, and databases, while these classes are further subdivided into sub-classes. The calculation of energy consumption places emphasis on particular components within each sub-class, encompassing CPUs, RAM, GPUs, hard drives, network switches, cooling systems, and communication units. Compared with existing green ICT standards where mainly focused on strategy-related recommendations, the methodology utilises manufacturers’ specifications for standard operational procedures in order to compute energy consumption. The objective of the methodology is to extend the current research and provide countable energy consumption attributes of various component types, that are used in the ICT architecture, supporting the decision-making process of architects regarding environmentally sustainable and high-performance device selection. This study concludes with the development of a tree diagram for calculating the energy consumption of an ICT system.

Analysis of Energy Consumption for Hollow Structure of 3D Printed Part Reinforced by Polymer Resin

Manufacturing time and energy consumption are two important parameters to evaluate the manufacturing process. The main purpose of this study was to analyze the energy consumption and printing time of 3D printed part between hollow and without any hollow structure. The specimen models were designed according to ASTM D368-IV standards and printed using a 3D printer device. During the printing process, the wall thickness of specimen was varied into three different types, 0.4 mm; 0.8 mm and 0.12 mm. Furthermore, the cavity was filled by polymer resin as main material for strengthening the specimen. Time and energy consumption of manufacturing process was recorded. In addition, the strength of specimens was observed using universal tensile test machine. The results showed that the highest manufacturing speed was occurred on the specimen that had wall thickness of 0.4 mm. Besides that, its parameter had a lowest energy consumption than specimen that had wall thickness 0.12 mm. The wall thickness showed the significant parameter to reduce the energy consumption, printing speed and material usage..

A comparative analysis of energy consumption in conventional and electric vehicles

A comparative analysis of energy consumption in conventional and electric vehicles

A data-driven model for the analysis of energy consumption in buildings

Data-driven models are gaining traction in Building Energy Simulation, driven by the increasing role of smart metering and control in buildings. This paper aims to enhance the knowledge in this sector by introducing a practical method to analyse heating consumption. The methodology involves the analysis of hourly total heating demand and outdoor temperature measurements to create and calibrate Energy Signature Curves. Importantly, the building Energy Signature Curve is calibrated independently for each daily hour, resulting in a subset of 24 data-driven models. After calibration, a disaggregation algorithm is proposed to distinguish space heating from domestic hot water usage. The method also evaluates the building’s thermal inertia, examining the correlation between the hourly global energy consumption and the outdoor air temperature moving average. It also presents a methodology for improving the DHW heat consumption model. The methodology is applied to a case study of 51 buildings in Tartu, Estonia, with complete yearly demand measurements from the district heating operator. Thanks to the hourly calibration approach, R2 is 0.05 higher on average than the yearly Energy Signature Curve approach. The difference between estimated and measured annual energy consumption is 8% on average, demonstrating the practicality and effectiveness of the proposed method.

Comparative Analysis of Energy Consumption and Carbon Footprint of Additively Manufactured Solid and Lattice structure Tensile Specimens

Manufacturing significantly contributes to global warming due to its substantial carbon emissions. United Nations sustainable development goals support the reduction of carbon emissions in the manufacturing sector, which can be accomplished by making the manufacturing process sustainable with a minimal carbon footprint. This is also appropriate for novel manufacturing processes such as additive manufacturing. This study introduces the investigation of the additively manufactured specimen. Prior research delves into examining the impact on the energy consumption of solid specimens under distinct printing process parameters. Nonetheless, the influence of electrical energy consumption and total carbon footprint for the additively manufactured solid and lattice structure has yet to be investigated. The current study fills the research gap by assessing layer thickness and infill density on both specimens’ electrical energy consumption and total carbon footprint. The presented study offers insight into the impact of layer thickness and infill density for the solid and lattice structure specimens and their comparison of electrical energy consumption and total carbon footprints. The results demonstrated that a rise in an infill density directly correlates with increased energy consumption and carbon footprints. However, rising layer thickness resulted in a reduction in both power consumption and carbon footprints. Furthermore, it was observed that the triangular, octagonal, and hexagonal cellular structures manifest higher power consumption when the infill density is set at 50% and 80%, respectively. Moreover, when assessing a solid specimen at 100% infill density, the total carbon footprint exhibits increases of 12%, 21%, 23%, and 41% in comparison to triangular, octagonal, hexagonal, and square lattice structures, respectively.

R&D and Application of Smart Park Energy Control Platform Combined with MLM Technology

Abstract As the core of park intelligence, the smart park energy control platform realizes the efficient management and optimal use of park energy by integrating and applying IoT technology. This study focuses on the research development and application of a smart park energy management and control platform using IoT technology, firstly constructing the IoT smart park platform, designing the login module, the park information module, and the energy consumption analysis module, and describing the steps of the smart park energy collection algorithm, and then designing the model of the energy prediction and planning algorithms, i.e.In the construction of the LSTM heating temperature prediction model and the GRU network modeling. Testing and research can be done by using a smart park as an example. The empirical study shows that the construction of the IoT smart park platform can improve the accuracy of monitoring, and the difference between the actual collection results and the system monitoring results is no more than 0.03 kW. After optimizing the configuration, the park’s energy supply system can meet the electric heating and cooling loads with optimal efficiency. Through the comprehensive evaluation of the operation of the extensive energy information monitoring platform, the environmental pollutant emissions are reduced by about 20%, and the platform has a remarkable effect on energy saving and emission reduction.

Evaluation of Grid Support Capacity From Electrified Railways

High power level and temporal operation flexibility endow electric trains with great potential for improving grid resilience. It becomes crucial to quantify the grid support capacity (GSC) from large-scale electrified railways. This paper innovatively proposes a hierarchical train coordination and trajectory regulation method to evaluate and deliver the GSC from electrified railways. In the light of the mechanical energy consumption analysis of train running, the energy-oriented train operation model is proposed to avoid the nonlinear differential dynamics of train kinetics when coordinating large-scale trains. Then the aggregation and disaggregation of trains for grid support can be formulated as linear and convex quadratic programming problems respectively. To closely follow the reference power and enhance the feasibility of train operation, a novel iterative discretization approach is proposed to generate the precise reference trajectory. A sliding mode controller with disturbance bound adaption is devised to overcome the modeling errors and disturbances when deploying GSC in realistic environment. Comprehensive numerical examples verify the effectiveness of the proposed scheme, and demonstrate the significant grid support potential from collaborative electrified railways.

Energy Consumption Analysis of Convolutional Neural Networks

Energy Consumption Analysis of Convolutional Neural Networks

A novel direct reduction and alloying process for making additive manufacturing of titanium alloys greener

Additive manufacturing (AM) is considered an energy-efficient alternative to conventional subtractive manufacturing of metals. However, the high embodied energy of the feed materials undermines the merits of energy saving in AM. Spherical Ti-6Al-4V alloy powder, widely used in AM, is commercially produced by atomizing Ti alloys made from the Kroll process. This production route incurs high energy consumption due to low yield, the energy required to melt and break the molten metal into fine particles and make the primary titanium metal. This paper presents a novel, energy-efficient pathway for directly fabricating spherical Ti-6Al-4V alloy powder from oxides. In this process, raw metal oxides are reduced to form fine Ti-6Al-4V alloy powder, which is then granulated, sintered, and deoxygenated to produce the low-oxygen spherical powder. The integrated process is called the direct reduction and alloying (DRA) process. Spherical Ti-6Al-4V alloy powders produced by this process have low interstitial impurities, controlled particle size distribution, homogeneous compositions, and excellent flowability for AM applications. Energy consumption analysis suggests that the DRA process has the potential to significantly reduce the energy consumption of producing titanium alloy feedstock, thereby improving the environmental impact of additive manufacturing.

Building Energy Consumption Analysis of a University Academic Building: Modelling and Simulation

Building Energy Consumption Analysis of a University Academic Building: Modelling and Simulation

Energizing Inventory Management to Optimize Energy Consumption of Handling Shortages by Neutrosophic Fuzzy Trapezoidal Number

Embarking on the exploration of integrating environmental sustainability principles and neutrosophic fuzzy theory in inventory management, this study aims to effectively tackle shortages. It underscores the vital balance between economic efficiency and ecological responsibility in contemporary inventory management practices. Neutrosophic fuzzy theory emerges as a robust tool for navigating the inherent uncertainties in inventory optimization, offering a versatile framework for modelling intricate problems. Strategies for optimizing resource consumption and minimizing waste generation within inventory management are scrutinized, emphasizing the imperative of harmonizing economic objectives with environmental concerns. Introducing a novel framework that melds neutrosophic fuzzy with environmental metrics, the research aims to optimize inventory management processes while mitigating environmental impacts. Furthermore, it delves into the challenges of managing energy consumption, advocating for innovative approaches to address fluctuating energy prices, data limitations, and evolving regulatory requirements. Neutrosophic sets are introduced for energy consumption analysis and cost evaluation, showcasing their efficacy in managing uncertainty and variability in real-world scenarios. The study concludes with a Python-based analysis of neutrosophic mean in energy consumption, offering insights into central tendencies and uncertainties associated with energy-related costs. Utilizing visualization techniques to enhance comprehension and decision-making in energy management, this research contributes to advancing inventory management practices by integrating environmental sustainability principles and sophisticated mathematical techniques, thereby fostering more resilient and sustainable supply chain operations.