Analysis Of Energy Consumption Research Articles

Analysis of fly-around mission with spinning tether system for space station observation

This paper presents the mission scenarios of using spinning tether system to conduct space stations fly-around mission and validates its feasibility. The main challenge of fly-around mission lies in the difficulty of balancing low fuel consumption and long-term fly-around observation. To deal with this problem, a novel spinning tether system is proposed. Firstly, the fly-around process with spinning tether system is introduced, and the tether system is modeled based on Newton-Euler method with a novel description of spinning motion. Secondly, Given the unique structural limitations of space stations, two fly-around schemes and referenced fly-around trajectories are detailed. Thirdly, a backstepping controller is proposed for tracking the reference motion of fly-around satellites, and the fuel consumption among different fly-around schemes is compared and analyzed. In the end, numerical results validate that under the proposed control strategy, the spinning tether system can maintain a stable fly-around configuration in both the planar and vertical plane, the symmetrical formation configuration prevents the central space station from being affected by the motion of fly-around satellites. Moreover, energy consumption analysis indicates that tethered system can save 62.8 % of impulse compared to traditional schemes when flying in the planar plane, making it the most energy-efficient option.

Comparative Analysis of Gini Coefficient, GDP, Energy Consumption, and Transportation Modes on CO2 Using NARDL (Nonlinear Distributed Lag Autoregressive Model) for the USA

The significance of the transportation sector, notably in terms of the carbon emission factor, is an undeniable fact. Along with this fact, individuals’ transportation preferences depend on their income levels. In this context, when the issue is considered, the income level in the USA pushes people toward cheap air travel. The main reason for this is that it is cheap, accessible, and transports one to their destinations quickly. Thus, from the perspective of road transportation, bus transportation is popular among the public. The reason why both air and road transportation modes are empirically evaluated together through income distribution is due to the preference of the US people. In this context, the effectiveness of active transportation on both air and highways in the USA from 1975 to 2023 is investigated by taking into consideration the income distribution. Empirical findings obtained through the FMOLS, DOLS, CCR, and NARDL models demonstrate that all independent variables, including GDP, energy use, air transportation, and the Gini coefficient, affect carbon dioxide emissions. In addition, wavelet analysis is performed to comprehend the form of and fluctuations in the series, which are vital to monitoring the periodical changes.

Energy consumption analysis of trains based on multi-mode virtual coupling operation control strategies

Urban rail transit, as a main mode of travel, has always been concerned with the balanced relationship between energy consumption and transport efficiency. The advent of the virtually coupled train set (VCTS) has significantly enhanced the efficiency of existing railway lines. Both energy consumption and comfort during the operation of VCTS control technology have emerged as significant challenges for the development of VCTS since the inception of onsite testing. A review of the current research status of VCTS control systems has revealed that the majority of current formation control systems are focused on the safe operation of formation trains, the stability of train control systems, and safe train separation models. However, a case study has shown there is a strong correlation between train operating intervals, train control strategies, and train operating energy consumption. Therefore, it is necessary to design and plan the operation modes of formation trains for different train operation requirements. This paper presents four distinct control modes, developed through the integration of the most prevalent VCTS control strategies, operational modes, and operational requirements. The design of these control modes is guided by considerations of energy consumption, safety, and comfort during train operation. A case study of Beijing Subway Line 11 revealed a significant correlation between energy consumption, control mode, and safety under different control modes. In particular, the correlation between train running intervals and energy consumption in a high-precision tracking state provides valuable technical support for the future operation of VCTS technology in different periods, scenarios, and environments.

A hybrid electro-thermochemical device for methane production from the air

Coupling direct air capture (DAC) with methane (CH4) production is a potential strategy for fuel production from the air. Here, we report a hybrid electro-thermochemical device for direct CH4 production from air. The proposed device features the cogeneration of carbon dioxide (CO2) and hydrogen (H2) in a single compartment via a bipolar membrane electrodialysis module, avoiding a separate water electrolyzer, followed by a thermochemical methanation reaction to produce CH4. H2-induced disturbances lead to efficient CO2 extraction without pumping requirement. The energy consumption and techno-economic analysis predict an energy reduction of 37.8% for DAC and a cost reduction of 36.6% compared with the decoupled route, respectively. Accordingly, CH4 cost is reduced by 12.6%. Our proof-of-concept experiments show that the energy consumption for CO2 release and H2 production is 704.0 kJ mol−1 and 967.4 kJ mol−1, respectively with subsequent methanation achieving a 97.3% conversion of CO2 and a CH4 production energy of 5206.4 kJ mol−1 showing a promising pathway for fuel processing from the air.

Analysis and prediction of energy consumption in office buildings with variable refrigerant flow systems: A case study

Accurately predicting building air-conditioning energy consumption is particularly important for energy management. However, for small datasets, there is a lack of simple and effective prediction methods applicable to multiple buildings. In order to fully explore the energy consumption mode of Variable Refrigerant Flow (VRF) air conditioning system and improve the accuracy of the model. This study uses the weather data of an outdoor meteorological data collection system and the air conditioning system and energy consumption data of an office building in Jinan. Initial analysis revealed that occupant behavior and daily variations in outdoor air temperatures significantly impact the energy consumption of air conditioning systems. Then, by applying the random forest algorithm and Pearson correlation analysis, the study identified daily average outdoor air temperature and VRF indoor unit running time as critical features for prediction. Subsequently, three machine learning models—Multiple Linear Regression (MLR), Back Propagation Neural Network (BPNN), and Long Short-Term Memory (LSTM) neural network—were developed and assessed using a 4:1 training-to-testing set ratio. Comparative analysis revealed that all models performed robustly, with the MLR model exhibited superior predictive accuracy (R2 (Coefficient of Determination) = 0.98, MAE (Mean Absolute Error) = 0.005 kWh/(m2·day), and RMSE (Root Mean Square Error) = 0.007 kWh/(m2·day) for the testing set) and simplicity. Furthermore, the effectiveness of the MLR model in handling small sample sizes was confirmed through cross-validation of data from two additional office buildings, highlighting its suitability for predicting energy consumption of VRF air conditioning systems in office buildings.

Experimental study on the improvement of bamboo properties using amide polymer repair material based on the principle of self-energy dissipation

Traditional bamboo and wood components have different holes due to their own and external factors, and the appropriate hole repair technology is the key to improve the component's energy dissipation capacity. In this paper, based on the principle of in-situ free radical polymerization and hollow glass microsphere (HGM) reinforced polymer, PAM hydrogel (PAMHG) with better deformation recovery ability and PAM/HGM amide polymer (AP) with better mechanical properties were prepared. Based on the principle of self-energy dissipation of components, AP is used to improve the deformation capacity of hollow bamboo. The ratio of PAMHG and AP was optimized by single factor test and orthogonal test. The performance of AP was verified by bending test, SEM, energy consumption analysis，durability test and statistical analysis of small diameter round bamboo. The size of the bamboo sample is 250 mm (L)×15 mm (D)×2.5 mm (t), in accordance with LY/T 3347–2023 " Testing methods for physical and mechanical properties of small diameter round bamboo " (China). The results showed that an appropriate amount of glycerol could effectively improve the volume stability of the hydrogel. HGM effectively improved the strength of the polymer, and when the mass ratio of HGM/PAM was 0.48, the maximum compressive strength was 0.8 MPa, which was 2.4 times higher than that of the undoped HGM, and at this time, the optimal polymer ratios were 25 g of AM, 1 g of MBA, 0.01 g of APS, 2 g of TEA, 2 g of Carbomer SF-1, a volume ratio of 1:1 of glycerol/deionized water that the dispersion medium was 70 ml, and HGM was 12 g. The average energy dissipation of grouted bamboo at ultimate load was 2.02 times that of the control group, and the highest bending strength and elastic modulus were 128.01 MPa and 11.24 GPa, which were 41.39 % and 39.75 % higher than that of the control group, respectively. The SEM results revealed that the AP partially prepolymerized solution could infiltrate into the cells and pores of the bamboo material to achieve filling, bonding, as well as reinforcing the bamboo tissue. In addition, the durability test shows that AP has good resistance to environmental pressure. The results of this paper can provide a reference for the restoration materials of bamboo and wood components.

Experimental Investigation of Energy Consumption in Select Micromobility Vehicles

<div>This study provides a detailed energy consumption analysis of two popular micromobility vehicles—an e-scooter and an e-bike—under various conditions, including steady-state and dynamics scenarios. Employing a custom-built data acquisition system, the research tested these vehicles in throttle mode, additionally assessing the e-bike across three pedal-assist levels. The findings reveal that the e-bike operates significantly more efficiently than the e-scooter, with both vehicles demonstrating peak power outputs significantly exceeding their rated values. Furthermore, the study explores how cargo affects the e-bike’s energy use, along with the charging and discharging behaviors of both platforms. Notably, the e-scooter exhibited a considerable battery self-depletion rate, a characteristic not observed on the e-bike.</div>

Анализ влияния климатических факторов на потребление электроэнергии предприятиями в неперевозочных видах деятельности ОАО «РЖД»

Purpose: analysis of the costs of energy consumption by enterprises of JSC Russian Railways by type of fuel, identification of the most costly type of energy. Systematization of factors influencing energy consumption. Characteristics of climatic factors affecting energy consumption. The task was set to assess the possibility of using ASKUE data to improve the system for analyzing and planning energy consumption in non-transportation activities of JSC Russian Railways. Analysis of climatic factors affecting electricity consumption by enterprises in non-transportation activities of JSC Russian Railways. Determine the maximum time interval for analyzing energy consumption data. Methods: collecting information on electricity consumption and changes in climate characteristics. The main research method is the method of correlation analysis, which allows us to identify the most important factors influencing electricity consumption. Based on the correlation coefficient, a correlation matrix will be compiled using the Statistica software package. Results: climatic factors have been identified that have the greatest impact on the energy consumption of the enterprise in non-transportation activities of JSC Russian Railways. The maximum enlarged interval for analyzing energy consumption data has been clarified. Practical significance: climatic factors have been proposed, the characteristics of which must be relied upon when planning electricity consumption at non-transportation enterprises of JSC Russian Railways. These factors can be used in the further development of a model for planning energy consumption. The maximum enlarged interval for analyzing influencing factors has also been identified, allowing to obtain accurate results. The possibility of using hourly ASCAE data to assess and identify climatic factors affecting electricity consumption by non-transportation enterprises has been proven.

Study on the operation strategy of the water cooling system in the single-phase immersion cooling data center considering cooling tower performance

Energy analysis from an entire systems perspective can further improve the energy efficiency of single-phase immersion cooling technology. In this study, a water cooling system for the single-phase immersion data center was developed using a combination of FLOWNEX SE and FLUENT software. Firstly, the energy consumption analysis was carried out from the perspective of cooling system, and an optimal operation strategy was identified to minimize energy consumption. Furthermore, the influence of ambient temperature and chip power on the operation strategy and energy consumption were analyzed, and the optimal operation strategy under different conditions was given. Finally, a comparison was made between the two operating modes of the water cooling system. The analysis results showed that there was an optimal operation strategy which should be adapted based on ambient temperature and chip power to reduce the power consumption. Moreover, it was noted that when the environmental temperature rises above 22 °C, the PUE of the water cooling system surpasses 1.2, indicating that the water cooling system becomes unsuitable for single-phase immersion data centers in such conditions. In addition, the excessive cooling of the chip can be avoided through the optimal operation strategy. And when the chip power was 600 W, the optimal operation strategy can reduce the PUE by 16 % and save 5.71 kW energy.

Energy consumption and economic analysis of CO2 capture from flue gas by membrane separation coupled with hydrate method

Hydrate-based CO2 capture (HBCC), as a new gas separation technique, has been widely studied. CO2 hydrate formation requires a higher pressure and lower temperature. In this study, a novel integrated process using membrane separation coupled with hydrate method was proposed to improve the efficiency of CO2 capture. The CO2 separation process from flue gas was simulated and optimized using Aspen Plus, revealing that the CO2 concentration was increased from 15 % to 42.32 % after membrane separation, and further rose to 87.35 % following Hydrate-based gas separation. The energy consumption for CO2 capture was calculated to be 2.81 GJ/tCO2, with an exergy loss during the hydrate-based separation stage reaching up to 33.26 %. The total cost of CO2 capture, including equipment investment, was determined to be 82.35 $/t, with an estimated payback period of approximately 6 years. Thus, the process using membrane separation coupled with hydrate method was reasonable and feasible through the energy, exergy and economic analysis. This research introduces a promising large-scale industrial CO2 capture process for the advancement of Hydrate-based CO2 capture technology.

실도로 주행시험 기반 전기자동차 공차중량 변화에 따른 에너지 소비패턴 비교 분석

실도로 주행시험 기반 전기자동차 공차중량 변화에 따른 에너지 소비패턴 비교 분석

A comparative analysis of the carbon footprint in green building materials: a case study of Norway.

This paper critically examines the carbon cycle and environmental impacts associated with building materials, encompassing diverse impact categories for both midpoint and endpoint scenarios. The research encompasses a comparative analysis of five distinct scenarios, contrasting the environmental performance of green against conventional counterparts. Notably, previous research endeavors did not investigate the effects of varying percentages with and without phase change materials (PCM). The primary objective is to assess the impact of integrating phase change materials (PCM) with varying percentages of fly ash (20% and 35%) on energy consumption and carbon emissions, particularly in cold climates like Norway. The study employs the ReCiPe2016 Midpoint (E) method, which offers a robust life cycle assessment (LCA) framework aligned with European standards, making it particularly suitable for this context. Energy Plus, within the Design Builder software, was used to simulate and calculate the impact of PCM on energy efficiency. The findings underscore those environmental impacts attributed to green buildings amount to 9.79 × 104 kg of CO2 equivalent, while conventional buildings account for 1.04 × 105 kg of CO2 equivalent. Furthermore, among the cases studied, the optimal scenario pertains to a green building utilizing 35% wind ash cement and PCM, resulting in the equivalent of 9.68 × 104 kg of CO2 emissions. Remarkably, the best-case scenario involves a green building boasting a robust steel interior structure and aluminum windows, whereas the worst-case scenario entails a typical building devoid of PCM implementation. Furthermore, energy consumption analysis indicates that scenario 5, which utilizes PCM and 35% fly ash, achieves a 15% reduction in cooling energy and a 6.9% reduction in heating energy compared to scenario 3, resulting in an annual energy consumption of 97,453.09 kWh.

System for the Acquisition and Monitoring of Energy Consumption in the Context of Industry 4.0

This paper examines methodologies for monitoring energy consumption and operational parameters of an industrial assembly line, with a particular emphasis on the utilization of virtual tools, augmented reality, and real-time data visualization. The research examines the integration of an interface and the implementation of a data acquisition system, employing augmented reality to facilitate interaction with the collected data. The proposed system employs the Node-RED interface to facilitate the establishment of connections between constituent elements. The data is stored in a database, which provides support for decision-making in the analysis of energy consumption and operational parameters of the industrial assembly line. The results demonstrate that the solution is effective in enhancing energy resource management, identifying inefficiencies, and optimizing the performance of industrial equipment. The findings indicate that the implementation of such a system can markedly enhance the industrial process.

Energy consumption analysis and operating characteristics research on small time scale of variable refrigerant flow air conditioning systems in public buildings

In order to analyze the energy consumption and operation characteristics of the variable refrigerant flow (VRF) air-conditioning system on small time scale and put forward effective energy-saving suggestions, the VRF system operation experiments are carried out under different indoor set temperatures, different indoor unit opening quantities and different indoor set air velocities. This experiment studies the main influencing factors of energy consumption and operation of the VRF system, such as indoor temperature difference, indoor air velocity, outdoor environment temperature and so on. The results show that the rated power of indoor unit is linearly related to the energy consumption of the VRF system. The total active power changes greatly when the VRF starts and stops, and the outdoor ambient temperature changes. The indoor set air velocities have a great influence on the energy consumption and operation of the VRF system. The performance parameters increase first and then decrease with the increase of compressor frequency and load rate. When the load rate is 40 %∼60 %, the performance parameters can reach 3.2 ∼ 4.2.

Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.

A Cross-Layer Approach to Analyzing Energy Consumption and Lifetime of a Wireless Sensor Node

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

BIM and orthogonal test methods to optimize the energy consumption of green buildings

The construction industry’s rapid growth significantly impacts energy consumption and environmental health. It is crucial to develop optimization strategies to enhance green building energy efficiency and encompass comprehensive analysis methods. This study aims to introduce and validate a novel framework for optimizing energy efficiency design in green buildings by integrating Building Information Modeling (BIM) technology, Life Cycle Cost (LCC) analysis, and orthogonal testing methods, focusing on enhancing energy efficiency and reducing life cycle costs. The optimization parameters for the building envelope are identified by analyzing energy consumption components and key green building factors. The orthogonal testing method was applied to streamline design options. Building Energy Consumption Simulation (BECS) software and LCC analysis tools were employed to calculate each optimized option’s total annual energy consumption and the current life cycle costs. Using the efficiency coefficient method, each optimization scheme’s energy consumption and economic indicators were thoroughly analyzed. The framework’s validity and applicability were confirmed through an empirical analysis of a campus green building case in Fujian Province, demonstrating that the optimized framework could reduce energy consumption by 4.85 kWh/m2 per year and lower costs by 38.89 Yuan/m2 compared to the reference building. The case study highlights the framework’s significant benefits in enhancing environmental performance and economic gains. The results provide critical parameter selection and offer scientific and technological support for the design of building energy efficiency, promoting optimization techniques and sustainable development within the construction industry.

Analysis of Indoor Air Quality and Fresh Air Energy Consumption Based on Students’ Learning Efficiency under Different Ventilation Methods by Modelica

This paper aimed to explore a suitable ventilation method at a lower cost of energy to pursue a high learning efficiency based on the characteristics of a Chinese student group and campus building. Firstly, the model was established by Modelica and a good agreement between the numerical simulation and the results by CONTAM 3.4.0.3 was obtained. Secondly, the effects of the fixed window-opening ratio method (FWM), switch control window-opening ratio method (SCM), and automatic control window-opening ratio method (ACM) on CO2 concentration, indoor air temperature, and the heating capacity of air conditioning were investigated. The results showed that, when the FWM with 0% opening or 20% opening was adopted, the indoor CO2 concentration (ICC) was higher than the limit value of the classroom air quality standard, which was 1000 ppm. When the fixed window-opening ratio was greater than 40%, the indoor air temperature could not be controlled at the set value of 18 °C, which presented bad indoor thermal comfort. Meanwhile, when the ACM was adopted, the duration to meet good indoor thermal comfort was 57.17% higher than that of the SCM. However, both of them could maintain the average ICC below the set value in the class. Lastly, the fresh air energy consumption under different ventilation methods was compared. When the design temperature was 13.5 °C, it could be revealed that the fresh air energy consumption under the ACM, SCM, and FWM with 40% opening was 46.58%, 48.38%, and 51.26% lower than those at 18 °C. In summary, it was recommended to set the design temperature of the classroom at 13.5 °C, and the ACM was suggested as a suitable ventilation method to provide fresh air for the classroom.

Nanotechnology for thermal comfort and energy efficiency in educational buildings with a simulation and measurement approach in BSh climate

Educational buildings have a large share and impact on urban development. While research shows a significant portion of non-industrial energy consumption in these buildings, obtaining optimal thermal comfort in educational buildings remains one of the main concerns in achieving the grounds to promote students’ best performance and efficiency. Extensive research has been done in this field, however, this research presents a new approach to the diverse use of nanotechnology techniques which improve its properties and components in the buildings, aiming to reduce energy consumption and increase thermal comfort. In this paper, thermal comfort and energy consumption are evaluated in a 12-class elementary school located in Shiraz City. Aeropan and nano-Phase change materials (nano-PCMs) is used in the window glass and walls of the studied case. This evaluation presents the simulation and experimental analysis of thermal comfort (PMV) and energy consumption of three classroom alignments in the school building including the Linear-shape (LS), the Integrated Linear-shape (ILS), and the U-shaped (US) alignment. The simulation was performed using EnergyPlus 9.6 software, while the experimental data was collected using TESTO 425 device. The result of this research shows that after applying nano-PCM and Aeropan techniques in window glass and walls, the US alignment has the highest reduction in energy consumption (monthly average of 11.80%) compared to LS and ILS alignments. This alignment includes an energy consumption reduction of 12.03% in the coldest, and, 11.66% in the hottest day of the year in addition to increasing the monthly average thermal comfort of school by the use of nanomaterials.

Reshaping water distribution topology from the carbon emission reduction perspective: An exploratory analysis of water distribution reliability

As urban economies continue to evolve, the water distribution networks (WDNs) are expanding in scale and becoming more interconnected, leading to increased carbon emissions from operations and maintenance. Consequently, enhancing the stability and safety of WDNs while saving energy has emerged as a primary research focus. This study abandoned the original use of high economic costs for post-maintenance of WDNs. Instead, it reshaped the traditional water distribution topology to form a dynamic, storable, energy-efficient "WDN self-help" model. Drawing inspiration from the "deep tunnel" project in drainage systems, the proposal was to leverage underground spaces to create a deep aqueduct (DA) complementing the traditional WDN, forming a three-dimensional (3D) WDN. Hydraulic and water quality analyses of varying scales of the 3D WDN model demonstrated its superior ability to equalize node pressures, reduce pipeline head losses, and maintain water quality for end-users. Reliability assessments of the 3D WDN revealed enhanced system robustness for medium-to large-scale distributions, while energy consumption analyses indicated a significant increase in water supply energy utilization and significant long-term reductions in carbon footprint. A practical case study was presented to validate the effectiveness of the 3D WDN concept, confirming its ability to reliably distribute water even in the event of a failure. Finally, an estimate of the retrofit cost and the static payback period of the 3D WDN was conducted. This study aims to provide a theoretical reference for the renovation of water supply projects or the optimal design of new WDNs in the context of carbon neutrality.