Evaluation Of Energy Consumption Research Articles

Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China

Vehicle electrification holds significant potential for driving both decarbonization and improved energy efficiency within the realm of road transportation. However, accurate quantification of energy consumption advantages offered by electric vehicles (EVs) has remained elusive, thus impeding their long-term development due to variations across cities and vehicle types. To address this challenge, we propose a novel regional vehicle energy consumption assessment framework that integrates a region-specific driving cycle (DC) database and advanced high-precision energy consumption models. The regional DC database is constructed using a Markov chain approach. The database is stochastic and diverse, while taking into account real driving features and regional geographic environments. Energy consumption models for internal combustion engine vehicle (ICEV), plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) are constructed based on machine learning approach. These models have good generalization ability and prediction accuracy, with R2 of 0.86, 0.86 and 0.77 on the test set, respectively. According to the interpretability of the models, vehicle acceleration and vehicle specific power are considered to be the most important features affecting vehicle energy consumption. According to the evaluation results, compared to using ICEV, the promotion of PHEV and BEV in Xining, a plateau city, can reduce energy consumption by 27.83 % and 32.05 %, respectively. The framework not only unifies the scale of energy consumption evaluation among different vehicle types, but also its good generalization ability can be migrated and extended to the evaluation of vehicle energy consumption in various regions. The establishment of this framework will contribute to the promotion and popularization of electric vehicles, as well as the advancement of energy-saving initiatives in the transportation sector.

Optimising building performance for a resilient Future: A Multi-Objective approach to Net Zero energy strategies

The escalating impacts of climate change on urban environments underscore the imperative for energy-efficient buildings. As the building sector constitutes approximately 40% of global energy consumption, the implementation of NZE designs is critical for emission reduction. This study utilised Building Performance Optimisation methodologies alongside a multi-stage optimisation framework to evaluate energy consumption, Life cycle cost and carbon emissions under various climate scenarios (RCP 2.6, 4.5, and 8.5) in three Algerian cities—Constantine (semi-arid), Algiers (Mediterranean), and Ghardaïa (arid).The results revealed that the integration of passive, active, and renewable energy strategies significantly curtailed energy consumption and CO2 emissions across different climatic contexts. Specifically, passive measures achieved reductions in energy use ranging from 39% in Ghardaïa to 15% in Constantine. Active systems, including high-efficiency HVAC technologies, resulted in energy savings of 59% in Ghardaïa, 52% in Algiers, and 47% in Constantine. Furthermore, renewable energy sources, with a focus on solar photovoltaics, contributed additional reductions: 44% in Algiers, 26% in Ghardaïa, and 20% in Constantine. The multi-stage optimisation approach also enhanced computational efficiency, significantly reducing simulation time and facilitating the rapid identification of optimal strategies.These findings highlight the efficacy of integrating a range of energy strategies to achieve NZE. Nevertheless, while these systems demonstrate robust performance under current conditions, their effectiveness diminishes under future climate scenarios, underscoring the necessity for adaptive and resilient design approaches. Ongoing optimisation and innovation will be essential to maintaining the efficacy of NZE strategies in the face of extreme future climate conditions.

Design of a continuous plasma activated water (PAW) disinfection system for fresh produce industry

This study introduces an innovative, industrial-scale continuous Plasma Activated Water (PAW) system, specifically designed for the fresh produce industry and optimized for micro to small enterprises. The system leverages a novel non-uniform electrode design to enhance average field strength, driving the efficient generation of reactive oxygen and nitrogen species (RONS) for steady-state PAW production. Key components include a venturi bubble generator for optimized gas-liquid interaction and a plasma reactor integrated with a continuous spray and drying setup, enabling consistent PAW production and application. Our preliminary results indicate about 1.5 log10 CFU/g decrease in microbial load on treated produce, which further decreased to 2.5 log10 CFU/g towards the end of storage life for tomatoes, with 20 kV applied to the reactors and a total residence time of 20 min. An evaluation of the maximum energy consumption of system indicated a process cost contribution of less than $0.5 per ton of treated produce. The promising initial results, scalable design, cost-effectiveness and sustainability aspects make this technology suitable for improving food safety while reducing chemical usage.

Benchmarking Heat-driven Adsorption Carbon Pumps (HACP): A Thermodynamic Perspective

Benchmarking is pivotal in standardizing industrial devices, leading to notable performance enhancements in fields such as heating pump air conditioning, photovoltaic devices, and more. The significance of treating the CO2 capture system in small/medium size was emphasized in this work as a standalone device from a thermodynamic perspective, which facilitates the creation of a comprehensive benchmarking methodology. In this study, we studied the heat-driven adsorption carbon pump (HACP) as a typical case for benchmarking. The benchmarking methodology proposed is structured through a five-step process: defining boundaries, determining indicators, establishing calculation processes, collecting and analyzing data, and ultimately evaluating and optimizing performance. By utilizing thermodynamic principles, the energy efficiency of HACP devices was assessed. Through the combination of standardized tests and theoretical calculations, this work enables a quantitative evaluation of energy consumption and the thermodynamic perfection of specific HACP devices.

Greenhouse Gas Emissions and Energy Consumption in the Garments, Textile, and Dyeing Sectors in Savar, Dhaka (2019-2021)

Greenhouse gas (GHG) emissions, a key driver of global climate change, continue to rise due to industrial activities and fossil fuel reliance. This study evaluates energy consumption patterns and greenhouse gas (GHG) emissions from the garments, textile, and dyeing industries in Savar Upazila, Dhaka, from 2019 to 2021. Natural gas has been identified as the dominant energy source, contributing to a steady increase in GHG emissions from 64,920 tons in 2019 to 69,531 tons in 2021. Diesel and electricity consumption also contributed to emissions, with notable growth in the textile and dyeing sectors. Although minimal, solar energy adoption showed a positive upward trend, rising from 0.058 to 0.113 TJ during the study period, indicating a shift toward renewable energy sources. Despite this, the total GHG emissions rose from 129,015 tons in 2019 to 137,880 tons in 2021, emphasizing the need for cleaner technologies and more sustainable practices. The findings underscore the significance of enhancing energy efficiency and implementing renewable energy sources to mitigate the environmental impact of these industries.

Unveiling the synergism and reaction pathway of ethyl acetate removal in DBD/absorption integrated reactor

The experiment employs dielectric barrier discharge (DBD) combined with absorption integrated reactor (DCAIR) for higher concentration ethyl acetate removal and studies the synergism of DCAIR technology and the reaction pathway of ethyl acetate. Among three technologies of DCAIR, DBD and DBD combined with absorption series reactor (DCASR), DCAIR achieved the highest removal efficiency (RE) of ethyl acetate at similar input power. Fixed gas flow rate of 1000 ml/min and cC4H8O2in of 5000 mg/m3, the average REs of DCASR and DCAIR are higher than that of DBD by 31.2 % and 34.2 %, respectively. For the efficiency compensation mechanism, one of synergies in DCAIR, RE of ethyl acetate could exceed 80 % at the input power of 15 W. Energy consumption evaluation shows when RE > 80 %, the max energy efficiency of three technologies is in the order 70.0 g/kWh (DCAIR) > 5.1 g/kWh (DCASR) > 3.0 g/kWh (DBD). Tail gas and absorbent analysis reveals DCAIR can enhance mineralizing ethyl acetate and eliminating the secondary pollutants (e.g., O3, gaseous organic intermediates, and COD, etc.) effectively, due to its special structure of reactor. According to FT-IR spectra, the degradation mechanism and reaction pathway of ethyl acetate in DCAIR has been unveiled.

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS.

Research on energy consumption evaluation and energy-saving design of cranes in service based on structure-mechanism coupling

Aiming at the problem that the life cycle energy consumption index of hoisting machinery is difficult to be objectively quantified, a crane energy consumption evaluation calculation method based on the working cycle of the service period is proposed, the typical service working mode of the crane is analyzed, and the working cycle process of the mechanism is clarified. On this basis, the stress cycle characteristics of the crane structure during the service period are determined based on the coupling relationship between the mechanism and the structure, which lays the foundation for accurately evaluating the life index of the crane. According to the relationship between the load rate and effective power of each mechanism during the service period of the crane, the calculation method for the evaluation and calculation of the structure energy consumption during the service period of the crane is studied. Finally, the crane is designed based on the concept of absolute service safety, and an optimal design model of the main girder structure of the crane with the goal of the lowest energy consumption in service is established. Using the research method of this paper to study a certain type of bridge crane, the energy consumption of the hoisting machinery during its service period is not only related to the accumulation of the operating characteristics of the mechanism but also related to the characteristic parameters such as the structure self-weight. The comprehensive evaluation of energy consumption provides an effective quantitative method and a reliable green design theory for crane design.

Evaluation and Analysis of the Wi-Fi HaLow Energy Consumption

Evaluation and Analysis of the Wi-Fi HaLow Energy Consumption

Multi-objective optimization of switchable suspended particle device vacuum glazing for comfort and energy efficiency in school typologies under hot climate

This study focuses on the multi-objective optimization of a switchable Suspended Particle Device (SPD) vacuum glazing system to reduce energy use while ensuring thermal and visual comfort in schools located in arid desert environments. A detailed synergy analysis was performed on a representative floor from three school classroom typologies, comparing suspended particle device vacuum glazing in its OFF and ON states against conventional double-glazing with air gaps windows. This analysis employed an advanced simulation framework integrating Rhino-Grasshopper with Open Studio Model, Colibri 2.0, and Design Explorer2 software to evaluate energy consumption, Adaptive Comfort Percentage (ACP), and Useful Daylight Illuminance (UDI). Thermal comfort varied across typologies and orientations, with the atrium with skylight typology frequently meeting or approaching the 80 % Adaptive Comfort Percentage benchmark. However, the southern and eastern classrooms in the enclosed atrium with clerestory typology did not exceed a 39 % adaptive comfort percentage. The comprehensive parametric and multi-objective optimization simulation revealed that suspended particle device vacuum glazing in its 'OFF' state did not fulfill the UDI300lux–1000lux criteria across all typologies, even with maximal Window to Wall Ratio (WWR) and skylight ratio (SR). However, the enclosed atrium with clerestory, integrating SPD vacuum glazing with a 40 % Window to Wall Ratio configuration, emerged as particularly effective in achieving optimal useful daylight illuminance distribution, minimizing energy consumption, and ensuring satisfactory adaptive comfort percentage across various orientations. These findings underscore the potential of multi-objective optimization of suspended particle device vacuum glazing, combined with strategic Window to Wall Ratio adjustments, as a viable alternative to traditional glazing systems in hot desert climates. The adaptability of suspended particle device vacuum glazing highlights its significant potential for achieving thermal comfort, enhancing sustainable architectural design, and improving energy efficiency in educational buildings within extreme climate zones.

Research on the Design of Green Roofs for Office Buildings in Xuzhou Based on Building Energy Consumption Evaluation

The roof is the part of a building that is exposed to solar radiation for the longest period, making green roofs particularly effective in reducing air conditioning energy consumption during the summer. This study aims to assess the advantages of modular green roofs in terms of energy savings and cost reduction during the summer in Xuzhou. By conducting field measurements and surveys under both air-conditioned and non-air-conditioned conditions and utilizing building energy simulation tools, the performance of green roofs with different parameters was compared. Using EnergyPlus, factors such as soil thickness, thermal conductivity, and leaf area index were simulated. The results indicated that green roofs have superior thermal performance in summer, with the daily cooling load per unit area for top-floor rooms being 1.05 kWh/m2, 0.21 kWh/m2 lower than that for bare roofs, achieving an energy saving rate of 16.7%. It is recommended that soil thickness not exceed 0.3 m and insulation thickness not exceed 0.05 m or be set to 0 m. Take building no. 2 of the Xuzhou material market as an example: with the optimized green roof, the energy saving rate increased to 27.0%, which is 12.4% higher than that of the original green roof. The suggested cost for modular green roofs is 204 RMB/m2.

Evaluation of Human Energy Consumption in Paddy Cultivation Practices

Evaluation of Human Energy Consumption in Paddy Cultivation Practices

A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

Accurate machine learning (ML) predictions for the early stages of the building design are crucial to construct energy-efficient buildings utilizing limited resources. Several studies have employed ML methods for energy consumption (EC) prediction without considering the utmost crucial PCM-integrated building design parameters. In addition, reducing the dataset by considering only the most significant building design parameters before applying the ML-based method would be beneficial for reducing the computational power and memory usage of the system as well as utilizing less time in the modelling process. To this end, this research presents a novel framework to establish the most robust and reliable ML-based prediction model with less complexity, considering only the most influential PCM-integrated building design parameters. These parameters were identified for future scenarios of hot semi-arid (BSh) climate zones using multi-stage sensitivity analysis. Afterward, a reduced EC database based on the most significant building's early-design-stage parameters (EDSPs) was utilized to formulate several multi-expression programming (MEP) and support vector machines (SVM)-based forecasting models, considering the variations in their hyperparameter values. Formulated prediction models have shown less time utilization through the training and testing phases for the EC evaluations of selected PCM-integrated building compared to the physical-modelling process. Several statistical parameters were used to test and validate the performance of the formulated prediction models. The acquired model evaluation and validation results demonstrated that the MEP-based prediction model (MEP15) exhibited the highest level of reliability and accuracy, showing an R2 value of >95% for both the training and testing phases. The model's interpretability showed that, throughout the parametric analysis, the developed prediction model adhered to the system's physical boundary constraints. Also, the best-performing prediction model showed energy savings of up to 12% for building integrated with PCM having a melting temperature of 28 °C. Conclusively, this research demonstrated that the developed MEP-based prediction model could be employed to precisely forecast the EC for selected PCM-incorporated building in the whole BSh climate, considering important EDSPs while utilizing minimal resources.

Comprehensive evaluation of product characteristics and energy consumption in hydrothermal carbonization of food waste anaerobic digestate: A perspective on phosphorus recovery and fuel properties

Food waste digestate (FWD) is rich in organic matter and phosphorus(P) elements, making it a potential source for energy and P recovery. Hydrothermal carbonization (HTC) addresses FWD's dehydration and stability issues, improving hydrochar's fuel properties and P recovery in hydrothermal liquid. Experiments with various additives reveals 1M HCl's effectiveness, increasing P recovery by 88.31 % at 120 °C, decreasing hydrochar ash content by 21.42 %, and raising heating value by 45.01 %. Principal component analysis highlights the viability of acid treatment for P leaching and hydrochar enhancement, with pH as a crucial factor. HTC substantially reduces energy consumption by 57.42 % compared to direct drying. Hydrochar without additives shows potential as slow-release P fertilizers, with a 28.8 % increase in total phosphorus at 240 °C, where 99.4 % of P exists as apatite inorganic. Soil tests reveal differences with formic acid method in P availability, showing varying correlations with different P forms. This research offers insights for FWD resource utilization.

Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

Energy audits in the tofu industry; an evaluation of energy consumption towards a green and sustainable industry

Energy audits in the tofu industry; an evaluation of energy consumption towards a green and sustainable industry

Integrated Optimization Study of Electric Bus Fleet Transformation

With the increasing social demand for sustainable and environmentally friendly transportation, the transformation of public transportation systems has become an important issue. In this paper, three mathematical models are developed to synthesize the ecological consequences, financial models and transition scenarios in the transition from diesel to electric bus fleet. In Problem 1, this paper adopts the Life Cycle Assessment (LCA) method to examine the full life cycle energy consumption of the product. The model comprehensively considers the main raw material production stage, vehicle assembly and distribution stage, vehicle driving stage and vehicle scrapping and dismantling stage, as well as the production stage of all types of energy added to the vehicle driving stage, i.e., the production stage of fuels and electricity, and finally applies Gabi, the life cycle evaluation software developed by PE Germany, to carry out the evaluation of the life cycle energy consumption and greenhouse gas emissions. Finally, the energy-saving potential of pure electric buses reaches 12.69%, and the GHG emission reduction potential reaches 6.05%. The results are applied to the bus fleet in Beijing, and it is calculated that the replacement of the bus fleet with pure electric buses can save 21.87% of energy and bring about a total GHG emission reduction of 60,700 tons. In Problem 2, this paper adopts the research method of life cycle cost (LCC). The model quantifies all the technical, material, human and organizational measures involved in the whole bus system process into cost indicators, and then builds a financial model for the transition of the fleet to a pure electric bus fleet based on the model, which provides a reliable basis for the bus company's decision-making. From a long-term perspective, the public transport company to choose a pure electric bus fleet will be more economically efficient, but need to bear the costly transition to the acquisition of costs. Overall, this paper comprehensively explores the smart bus fleet transformation problem through three complementary models. It provides strong support for decision makers from the perspectives of life cycle cost, financial planning and environmental friendliness. The combined use of these models makes the article more comprehensive and provides useful lessons for future bus fleet transformation.

Evaluation of upper-room ultraviolet germicidal irradiation system for air disinfection and energy conservation in multi-patient ward

High occupancy rates significantly increase the risk of airborne transmission of respiratory infectious diseases in multi-patient wards. While increasing air supply can effectively reduce airborne transmission, it also substantially raises energy consumption. Therefore, developing low-energy methods in multi-patient wards to suppress airborne transmission is essential. This study aims to evaluate the inactivation performance of upper-room ultraviolet germicidal irradiation (UVGI) against SARS-CoV-2 virus aerosols in a typical multi-patient ward using computational fluid dynamics at different ventilation rates, as well as its energy-saving effects. Upper-room UVGI reduced the virus aerosol concentrations, with a maximum decrease of up to 159 copies/m3 in the occupied zone (zone less than 2.1 m in height) observed at 8 ACH. However, increased ventilation rates limited the inactivation performance of upper-room UVGI. Two parameters of UVGI, irradiation height and irradiation flux, had been detailly evaluated. Elevating the irradiation height of upper-room UVGI could significantly enhance the inactivation performance. Increasing irradiation height from 0.3 to 0.6 m reduced the concentrations in the occupied zone by up to 11 copies/m3, while a further increase to 0.9 m decreased the concentrations by up to 55 copies/m3. However, increasing irradiation flux could only approximately linearly enhance the virus aerosol inactivation performance of upper-room UVGI. The energy consumption evaluation of upper-room UVGI shows that UVGI activation could conserve 100 kWh per year in one multi-patient ward, with a maximum saving of 333.7 kWh. These findings provide insights into the setups strategies and the energy-saving performance of upper-room UVGI systems in multi-patient wards.

Studies on energy consumption and other important machining characteristics in sustainable turning of EA1N railway axle steel

The present research focuses on comprehensively evaluating energy consumption and other vital machining characteristics during the turning process, aiming to optimize efficiency while minimizing environmental impact. The experimental data is collected through a series of machining tests on EA1N railway axle steel under dry, minimum quantity lubrication (MQL), and cryogenic cooling conditions. Under these cutting conditions, the machinability criteria (energy consumption, tool wear, surface quality, chip morphology) of train wheel axle steel were tried to be improved. As a result, cryogenic cooling at constant cutting speed gave 40% and 53% better results in terms of energy consumption than MQL and dry environment, respectively. When the same situation was examined in terms of tool wear and surface quality, 10–18% and 8–14% gave better results, respectively. In other words, it is worthy to mention that the research findings not only benefit the manufacturing industry by optimizing resource utilization but also align with global efforts to promote environmentally conscious practices in the engineering and transportation sectors.

A Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption

Existing methods for optimizing the LLVM compiler face challenge in quickly obtaining high-quality solutions due to the large and discrete optimization space of option sequences, time-consuming energy consumption evaluation, and complex interactions among options. To address these challenges, we present a Two-Stage LLVM Option Sequence Optimization Method to Minimize Energy Consumption (TSOMFEC). In the first stage, we propose an Option Selection algorithm based on Pattern Mining (OSPM). OSPM integrates a pattern mining algorithm and CRC algorithm to precisely preselect options, with the goal of reducing the search space of option sequences while guaranteeing optimization quality. In the second stage, we present an Option Sequence Evolutionary Algorithm (OSOA). OSOA encodes options and their positions in the sequence as individual items and then constructs an item interaction graph to capture complex interactions among different items. The item interaction graph is utilized in a genetic algorithm to reduce actual energy consumption evaluations and rapidly obtain high-quality option sequences. We evaluate the performance of TSOMFEC by comparing it with four state-of-the-art methods across eight typical embedded programs in terms of solution quality and optimization time. Furthermore, we validate the effectiveness of both OSPM and item interaction graph. The experimental results demonstrate that TSOMFEC not only achieves the best solution quality but also spends the least optimization time. It obtains average energy consumption improvement percentages relative to the -O3 level of 0.075, along with average optimization time of 3178 s. Moreover, the effectiveness of both OSPM and the item interaction graph has been validated.