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With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]

The pulp and paper industry accounts for approximately 5% of total industrial energy consumption and contributes 2% of direct carbon dioxide (CO 2 ) emissions from industries. World paper and paperboard demand and production are increasing significantly, leading to an increase in this sector's energy use and CO 2 emissions. Although current studies identify a wide range of energy-efficiency technologies that have already been commercialized for the pulp and paper industry, information is limited or scattered regarding new energy-efficiency technologies that are not yet fully commercialized. Development of emerging or advanced energy-efficiency technologies and their deployment in the market will be crucial for this industry's mid- and long-term energy saving and climate change mitigation strategies. This paper compiles available information on energy savings, environmental and other benefits, costs, and commercialization status for 25 emerging technologies to reduce the energy use and CO 2 emissions. The purpose is to provide a well-structured comprehensive review on these emerging energy-efficiency technologies for engineers, researchers, investors, policy makers, pulp and/or paper companies, and other interested parties. [ABSTRACT FROM AUTHOR]

Abstract With the advent of the Paris Climate Agreement and China ratifying it recently, there is a need to adopt a diversified approach to address climate change; this is especially the case of promoting residential energy conservation. This study is one of the first household energy intervention studies that focuses on the comparison of two message delivering means, paper-based versus instant messaging tool, as a platform for sharing energy-saving tips and engaging households to save energy in China. Conducted in several communities in Hangzhou, Zhejiang Province of China, the effectiveness of using a widely used application known as WeChat in promoting household energy conservation is compared with that of using stickers. It was found that WeChat is the most effective is reducing monthly consumption, but the effects are short-lived. Comparatively, using stickers as a mean of engaging households produces more sustained results in terms of energy savings. This study also provides evidence to correlate the changes in energy consumption behavior with personal perception of one's responsibility and quality of life. That is, certain behavior can be triggered if residents are willing to impose energy ration in their households or are given more opportunities in the form of local programs that enable them to have more practices in energy conservation. [ABSTRACT FROM AUTHOR]

Abstract: This work analyzed the energy flow of the pulp and paper industry in Taiwan. The potential technology options that were examined focus on how to capture some of the energy currently lost in the processes and then identifying the areas with energy-saving potential that could also have large impacts across a variety of industries. In addition, the energy-saving potential of these options was evaluated. The energy-saving potential of the pulp and paper industry would be around 6939.9 KLOE/M. The greatest energy-saving potential lies with improving energy distribution and equipment efficiency, which would together potentially comprise 86.8% of total energy conservation. This analysis can serve as a benchmark for current pulp and paper making operations, and as a base case for stimulating changes toward more efficient energy utilization in the pulp and paper industry. [Copyright &y& Elsevier]

Paper industry is one of major material and energy users in Finland, material costs being far bigger than energy costs. Energy saving is promoted in Finland by an energy service centre, subsidies and energy service companies (ESCOs). Material savings in production processes are typically used to reduce costs while life cycle material efficiency rather would require focusing, e.g. on product design. This could be promoted by total producer responsibility, economic instruments and environmental permits. For industrial sites with limited resources, MASCOs’ (Material Saving Companies) services could manage material savings, e.g. by funding the investments, planning and running the project. Authorities should put more emphasis on material auditing and information on material saving technologies and logistics. Material savings should also be included into the environmental permitting procedures. Materials saving services are developing and the projected Finnish material efficiency service centre could support new businesses. This article is based on a literature review and interviews of persons in paper producing companies and environmental authorities. We also analyse experiences of the first MASCO project in Finland. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to estimate the energy saving potential in China's paper industry by determining energy intensity under different scenarios. Cointegration model and stability test are applied to formulate the equilibrium equation. Results show that energy price, industry structure, profit margin and technology have significant impact as they are negatively correlated to changes in energy intensity. Three scenarios (BAU (business as usual), intermediate and active) are designed to estimate the future trend of energy intensity in paper industry. In 2010, the energy intensity (energy consumption per unit of value added) was 4 tce/10,000 RMB, and by the scenarios it is expected to considerably decline. According to the BAU (business as usual) scenario, the energy intensity will decrease to 2.56, 1.43 and 0.70 tce/10,000 RMB by 2015, 2020 and 2025, respectively. With respect to the intermediate scenario it is expected to drop further to 0.44 tce/10,000 RMB. Yet by the active scenario, the energy intensity is expected to considerably decline to 0.36 tce/10,000 RMB by 2025. Using the BAU forecast as a baseline, the quantity of energy savings is estimated to be 185.6 billion tce by 2025. Considering this important potential, we also provided some policy suggestions. [Copyright &y& Elsevier]

Abstract: The pulp and paper industry is one of the most energy-intensive industries worldwide. In 2007, it accounted for 5% of total global industrial energy consumption and 2% of direct industrial carbon dioxide (CO2) emissions. An energy audit is a primary step toward improving energy efficiency at the facility level. This paper describes a plant-wide energy audit aimed at identifying energy conservation and CO2 mitigation opportunities at a paper mill in Guangdong province, China. We describe the energy audit methods, relevant Chinese standards, methods of calculating energy and carbon indicators, baseline energy consumption and CO2 emissions of the audited paper mill, and nine energy-efficiency improvement opportunities identified by the audit. For each of the nine options, we evaluate the energy conservation and associated CO2 mitigation potential. The total technical energy conservation potential for these nine opportunities is 967.8terajoules (TJ), and the total CO2 mitigation potential is equal to 93,453tonnes CO2 annually, representing 14.4% and 14.7%, respectively, of the mill’s total energy consumption and CO2 emissions during the audit period. [Copyright &y& Elsevier]

Abstract: The systematic, transparent and accurate on-line energy information of the production process is quite the basis of mill energy management and conservation. An Energy Management System (EMS) was built according to the characteristics of the energy usage in a typical newsprint paper mill. The EMS first realized the process data acquisition and integration, second fulfilled the on-line energy information calculation from the integrated data. A water and steam properties query component based on IAPWS-IF97 was developed and plugged into the EMS to facilitate the energy calculation of water and steam. The practical performance in the paper mill showed that the integrated energy information of the whole mill was gained and the on-line energy supervisory was achieved. In the end a systematic, transparent and accurate energy usage profile is obtained and it would provide the valuable fundamental energy data for the further energy analysis and optimization in the EMS. [Copyright &y& Elsevier]

Abstract: Adoption of energy efficient technologies is considered a key sustainability strategy for reducing greenhouse gas emissions in global industries. This article aims to assess costs of energy savings and carbon reduction from applying energy efficiency technologies in the US pulp and paper sector. Applicable efficiency measures were identified and their cost effectiveness was evaluated in two historical years – 1994 and 2006. Potential savings of final energy use resulting from applicable sustainability options were estimated as 707 PJ in 1994 and 1064 PJ in 2006, respectively, corresponding to approximately 32% of the sector''s annual final energy use in 1994 and 62% in 2006. The associated carbon-emission reduction was 8.1 million ton of carbon (MtC) in 1994 and 11.8MtC in 2006, corresponding to 26% and 45% of sector''s total energy-related carbon emissions in 1994 and 2006, respectively. Using the concepts of cost of conserved energy and cost of carbon reduction, we estimated that cost effective sustainability options contributed to final energy savings in the range of 15–25% of the annual energy use, and carbon-emission reduction from 14% to 20% of annual carbon emissions from the sector. [Copyright &y& Elsevier]

[Display omitted] • Novel method to estimate total technical potential for improving energy efficiency. • Bottom-up sector energy model used to characterize energy demands and opportunities. • Technology-explicit analysis framework used to estimate integrated energy savings. • Application to Canada's pulp & paper sector indicates major savings are possible. • Technical potential to reduce natural gas use by 95% and net electricity by 54% Industrial energy efficiency has long been recognized as a critical tool for energy management and emissions reduction and is increasingly considered to be an energy resource in its own right. However, the complexity of most industrial sectors creates barriers for modellers, companies, and policymakers in understanding and achieving the overall potential for efficiency-driven energy savings. To address this gap, we have developed a novel method that combines disaggregated bottom-up sector energy modelling with analysis of a comprehensive set of technology-explicit energy efficiency measures. This approach overcomes the deficiencies of top-down analyses and better represents the complexities of multi-measure efficiency investments compared to piecewise studies of individual measures. In this study, we demonstrated application of our method to the case of the Canadian pulp and paper sector. Our analysis shows that the sector can reduce its natural gas and electricity use by 95% and 41%, respectively, by adopting economical efficiency measures. This equates to 71 PJ of natural gas and 44 PJ of electricity saved annually and would result in cost savings of $81 per tonne of output sector-wide. Notably, we find that efficiency and fuel switching have the technical potential to reduce natural gas consumption by 98% across the sector. Taken together, the results provide quantitative evidence that energy efficiency is an underestimated resource for cost-effective energy savings in the Canadian pulp and paper sector. Our analysis framework can be applied to any industrial sector in any region to provide insights regarding energy conservation strategies and policy design. [ABSTRACT FROM AUTHOR]

Abstract: Energy conservation is a central concern of the current industrial world, where increasing efficient energy usage is the only way of reducing a high energy demand. In the present study the optimization of a pulp and paper mill with a cogeneration plant has been carried out. The optimization was realized with a methodology which includes exergy, exergoeconomics, thermoeconomics and pinch analysis. The proposed methodology was useful in determining not only the best plant operating conditions but also establishing the components or subsystems with the highest irreversibilities. As a result of the study, operation changes in the recovery boiler, the turbogenerator, the thermal treatment and the deaerator were realized. Due to the higher irreversibility in the actual evaporator line, a new line of evaporators was proposed. Also, an innovative heat recycling technology as to the use of heat transformers was proposed in order to reduce waste heat discharged to the atmosphere. The results obtained with the proposed methodology, which integrates the different optimization methods, allowed reaching higher efficiencies and lower operational costs than those obtained with the optimization methods working separately. [Copyright &y& Elsevier]

The pervasive uncertainties in multiple port equipment scheduling frequently result in container handling delays or ineffective plans. To address the complexities and uncertainties of port multiple equipment integrated scheduling problem, this paper introduces a Digital Twin-driven (DT-driven) proactive-reactive scheduling framework for the first time. This framework is designed to promptly respond to uncertainties in the scheduling process and provide a transparent visualization of operational information. It specifically tackles the integrated scheduling problem of port quay cranes, Intelligent Guided Vehicles (IGVs), and yard cranes, considering uncertainties such as fluctuations in operating time, equipment failures, and IGV route conflicts. By developing a virtual container port simulation, which features a U-shaped port layout and double-cycling mode drawn from real-world scenarios, the paper evaluates the proposed framework's effectiveness. The experimental results demonstrate that the digital twin framework method significantly improves efficiency and conserves energy. Additionally, in large-scale conditions, the makespan difference between the DT-driven approach and the non-DT-driven approach is as much as 19.56 %. In terms of energy consumption savings, the DT-driven approach's scheduling plan can save 3.67 % of energy consumption under large-scale conditions. Moreover, as the fluctuation index increases, the energy consumption savings become even more significant. This paper also discusses the potential implications of adopting this framework for port companies, highlighting its benefits in enhancing operational and energy efficiency and its incorporation into port management systems. The sensitivity analysis can offer guidance to port companies on optimal equipment allocation strategies. [ABSTRACT FROM AUTHOR]

• A distribution dynamics approach is employed to examine the converge of TFEE across Chinese PAA level cities. • The findings show a clear convergence in TFEE. • Convergence comes more from efficiency drops in high TFEE cities than from efficiency gains in low TFEE cities. • The environmental protection polices in key cities improved energy efficiency. • Large-sized cities show better performance in energy efficiency than small- and medium-sized cities. Improving energy efficiency is considered the most direct route to reducing carbon emissions on a massive scale. To examine the long-run trend of China's energy efficiency, this paper employs a distribution dynamics approach to analyze the convergence of total-factor energy efficiency (TFEE) across 286 Chinese cities between 2002 and 2014. The result suggests the existence of convergence, which comes more from the efficiency declines in the high TFEE cities rather than the efficiency gains in the low TFEE cities. Despite the fact that a few cities have performed well in energy efficiency, the majority of cities have converged to a low-efficiency point. Further analyses show that the inefficiency was attributed to inefficient energy use in small- and medium-sized cities. Our analysis provides strong scientific support for China's ongoing energy conservation and environmental protection policies. [ABSTRACT FROM AUTHOR]

• The relationship between the buoyancy and gravity is analyzed when the thermal effects are considered. • Three station-keeping scenarios are proposed and compared for the stratospheric airship. • The significance of pressure control and altitude control is revealed by the calculation and analysis. • An effective way to specify the helium's mass is presented when the thermal effects are considered. • The airspeed control is shown as an efficient way to cut down the energy consumption during the long-endurance flight. The ability to achieve long-endurance station-keeping flights makes stratospheric airships desirable platforms for the provision of communication and surveillance services. To maintain long-endurance flights, it is necessary to consider the problem of energy consumption. In this paper, we discuss long-endurance flight scenarios of stratospheric airships in the presence of thermal effects. The balance between buoyancy and gravity is influenced by thermal effects during the diurnal cycle. We perform a theoretical analysis based on the helium's mass, pressure differential, and altitude as the main factors. To verify the effectiveness of the control over the pressure differential and the altitude, three long-endurance flight scenarios are proposed and compared. Then, the corresponding optimization problems are constructed to determine the energy-minimum flight. Finally, further efforts are made to reduce energy consumption. The realization and limitations of two strategies for improvement are analyzed. A comparison with other scenarios shows the effectiveness of energy conservation. The study in this paper thus provides a reference for station-keeping applications of stratospheric airships. [ABSTRACT FROM AUTHOR]

GEP (Generation Expansion Planning) is one of the most complex tasks in which planning is done to find the best possible distinct solutions to install new generating units to meet future load demands. This paper uses the Low Emission Analysis Platform (LEAP) software environment to analyse the GEP for the AP Power sector from 2021 to 2050. In recent years, the AP power sector has faced several issues, such as frequent service cuts due to power shortages. In this paper, five scenarios such as Business As Usual (BAU), Low Demand Growth (LDG), High Demand Growth (HDG), Least Cost Optimization (LCO) and Climate Mitigation Scenarios, have been analysed with environmental, technical, and economic parameters. This study has estimated the total environmental emissions and total cost of all scenarios. At present, Andhra Pradesh has a total installed capacity of 25.4 GW. The best scenarios can be selected based on environmental impact, cost-effectiveness, efficiency, and reliability. The results show that the LDG scenario will be an optimal choice for AP due to the state's economic crisis, and the government should also focus on energy conservation and limit demand growth to 4.5%. While presenting a general GEP, the study presents comprehensive system performance scenarios from which planners can choose. • Making a generation expansion planning • Making a sustainable environment • Presented a good solution for power generation • Analysed the total environmental emissions • Analysed the energy demands. [ABSTRACT FROM AUTHOR]

• BIM is used as a thinking tool for the conceptual design process. • The model is intended for the neutralization of energy consumption, estimating the amount of photovoltaic panels needed to achieve a NZEH. • A new, original methodology is used, with a built BIM prototype to measure energy consumption. • This tool for enriching the world with a new sustainable dimension is implemented in the BIM app as part of the software. • The originality of this paper is to create an embedded simplification tool for evaluating the electrical energy consumption (energy system) inside BIM. BIM is not simply a drafting tool or advanced CAD. BIM is a collaboratively generated and maintained data-rich information source, especially when it comes to the energy-calculation process and beyond. Due to growing awareness around energy conservation, numerous projects and initiatives have occurred to promote the development of low- and net-zero-energy homes. The widespread adoption of photovoltaic rooftop systems has shown that solar homes can be a effective solution to that energy demand; nowadays, the idea that BIM is not just software has been decided upon. In this paper, a new approach was used to calculate energy consumption using a BIM template designed for this purpose. The greatest value derived from BIM was in the template designed to neutralize energy consumption by calculating the number of photovoltaic panels needed to achieve a net-zero-energy house. The BIM prototype developed is considered a useful tool, intended to be used by everyday, non-specialist users to aid and disseminate energy knowledge in order to achieve a zero-energy home, as well as to be used as a decision-support tool, incorporating energy simulation into the early design process of zero-energy buildings in architectural practice. Finally, the tool was tested on a group of users in order to fine-tune and develop the design parameters of the template. [ABSTRACT FROM AUTHOR]

Energy conservation running for vehicle has been a promising research hotspot in the many universities and research institutions. In order to improve the energy utilization rate in the vehicle running process, an optimization method of the energy consumption and recycle based on fuel cell (FC)/supercapacitor (SC) hybrid tram is proposed in this paper. In the method, a tram operation energy management strategy based on Pontriagin's minimum principle (PMP) can effectively was proposed what adjusts the output power of FC and SC and decreases hydrogen consumption. In addition, a tram breaking velocity curve with maximum energy recovery and the allocation strategy between regenerative force and mechanical braking force be also studied in this paper. According to the simulation results, it could be obtained that the Energy conservation rate is about 5% higher than the un-optimized, it will effectively decrease hydrogen consumption. [ABSTRACT FROM AUTHOR]

Cities have emerged as critical and challenging places for promoting the green and low-carbon transformation of the economy and society in the context of the "dual carbon" targets. Meanwhile, fiscal policy is also an indispensable component of the public policy framework supporting the green and low-carbon transformation. This paper uses the pilot project of "Comprehensive Demonstration Cities for Energy Conservation and Emission Reduction Fiscal Policies" ("ECER-CD" policy) implemented in China since 2011 as a quasi-natural experiment. This research theoretically analyzes and empirically tests the energy conservation and emission reduction effects of fiscal policies supporting green and low-carbon transformation utilizing methodologies such as time-varying DID and multiple periods and multiple groups of DID. The study finds that the pilot cities effectively reverse the behavioural bias of local governments and promote energy conservation and emission reduction efforts. This conclusion holds after undergoing robustness tests such as parallel trend analysis and heterogeneity treatment effects. The effectiveness of the pilot policy is attributed to the upgrade of industrial structure, improvement of energy efficiency, and the effects of green technology innovation. However, cities' geographic location and resource dependency can interfere with the effective implementation of policies. This paper also further explores the environmental dividends and economic consequences of the policy and finds that the pilot cities have certain environmental dividends and do not significantly restrain economic and social development. This study broadens the research perspective on fiscal policies in energy conservation and emission reduction and clarifies the pathways through which fiscal policies promoting green, low-carbon transformation drive energy conservation and emission reduction in cities. Moreover, it also improves the use of the Difference-in-Differences method in evaluating policy effects in scenarios such as the overlapping implementation of pilot policies and policy withdrawal. Finally, this paper proposes policy recommendations to provide more empirical evidence for China to accelerate the green and low-carbon transformation of the economy and society. [ABSTRACT FROM AUTHOR]

• Summarize household energy consumption data characteristics and development trends. • Analyze household energy consumption patterns based on the time dimension. • Summarize the main influencing factors of household energy consumption. • Organically combine data, household energy consumption patterns and drivers. Understanding household energy consumption (HEC) demand patterns and their influencing factors at different times can provide insights into household energy demand and consumption behavior. Combining with optimization technology can improve energy efficiency, promote energy conservation, and predict future energy demand. Obtaining comprehensive and accurate data is the basis for revealing and analyzing HEC patterns and related activities. The main purpose of this paper is to review the current methods of obtaining HEC data in different time dimensions, HEC patterns, and influencing factors at different times, and organically combine all three. We first start with HEC data. This paper provides an overview of the current methods and data types used to collect information on HEC and related activities. It includes macro panel data, micro questionnaire survey data, micro case study data, and micro smart meter and advanced sensor data. Following this, presents current studies on HEC patterns, with a focus on cross-country comparisons in the temporal dimension. Direct and indirect (time-occupancy) studies are two ways of studying HEC patterns over time. Finally, discusses the factors that influence HEC demand, such as climate, building type, and socio-demographic factors. The understanding of these factors is crucial for promoting energy conservation and efficiency in households. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, energy conservation and carbon reduction in the heating industry have become the focus of attention. However, severe heat loss is currently occurring in the secondary heating system due to hydraulic imbalance, which is unable to supply heat according to users' needs. In view of this, a thermodynamic-hydraulic model of users' demand for heat and flow rate for the heating system is established. Then, a novel method to regulate hydraulic imbalance is proposed, with the dynamic heat demand satisfied by regulating the valve opening based on NSGA-II. The optimization results show that the comprehensive hydraulic imbalance coefficient has been reduced from 23.08 to less than 0.019, which means that almost 100% of users can get heat supplies corresponding to their needs, while the energy consumption is reduced by about 30%. Additionally, when there are 1000 population and iteration steps, good convergence and high speed are presented using this method. In summary, an on-demand optimized model of hydraulic balance for the secondary heating network is put forward, which provides powerful engineering support and guidance for intelligent heating construction. • This paper presents a method to regulate the hydraulic balance of network using NSGA-II according to the heat demand. • This paper combines the demand flow regulation of users with the resistance analysis of the heating network. • The hydraulic balance can be implemented greatly by using the hydraulic imbalance coefficient as the optimization goal. • The number of iteration steps in the algorithm has an effect on the optimization results. [ABSTRACT FROM AUTHOR]

The customers of textile wet processing plants have committed to reduce their greenhouse gas emissions from outsourced manufacturing activities. The textile brands directly use specific energy consumption values to analyze and set steep, time-bound emission intensity reduction targets for the energy intensive textile wet processing plants. However, the direct use of specific energy consumption values to evaluate energy savings can be misleading. In this paper, the authors propose a methodological framework to systematically evaluate energy performance and monitor energy savings in the textile wet processing plants. Further, the approach was extended to analyze the energy performance monitoring practices and identify energy conservation measures. The proposed framework was developed based on thorough analysis of the manufacturing process of textile wet processing plants and the principles of ISO 50001. A textile wet processing plant was chosen as a case study for illustrating the effectiveness of the framework using multiple regression method. Bottom up approach for benchmarking was used and yet significantly simplified by the introduction of an innovative parameter "program mix factor" and thereby evaluate "equivalent specific energy consumption". The predicted values for specific energy consumption were found to be within a close error range of ±10 % of the actual data. The proposed methodological framework is therefore innovative, simple and yet comprehensive to conduct internal benchmarking in textile wet processing plants. The textile brands are suggested to use equivalent specific energy consumption instead of specific energy consumption for energy performance monitoring of their textile wet processing plant suppliers. The authors also conducted a survey in fifteen textile wet processing plants to assess the prevailing energy monitoring practices. The results of the survey suggests that only 20 % of the plants are practicing basic level energy performance monitoring, while the remaining 80 % are at minimal level. • Textile wet processing plants were analyzed to identify the parameters required to normalize specific energy consumption • A simplified methodological framework was developed for the textile wet processing plants and the textile brands • The framework serves as a useful tool to evaluate, monitor the realistic energy performance & identify conservation measures • The textile brands are suggested to use equivalent specific energy consumption to monitor energy performance • The study identified the low levels of energy monitoring practices in textile wet processing plants in South Asian countries [ABSTRACT FROM AUTHOR]

Vacuum suction technology is a pivotal innovation within the Industry 4.0 revolution. Ejectors serve as the core of vacuum systems with distinct advantages such as simple structure, low cost, and ease of operation. However, traditional fixed ejectors suffer from low efficiency, energy waste, and poor adaptability. This study aims to optimize the design of vacuum ejectors to meet various requirements, achieving energy efficiency and high performance in automated production. On this basis, an energy-saving vacuum system based on dual ejectors is proposed, which can switch between single and dual ejectors depending on different working stages. When the vacuum reaches 90 kPa, the required motive pressure and the entrainment ratio are selected as the screening indicators for the vacuum ejector. A two-dimensional axisymmetric model is established and verified through experimentation. The key parameters are identified through a single-factor analysis. A multi-factor analysis is employed to determine the recommended values for each parameter and their respective applications. The energy-saving effects of the optimized ejectors are evaluated in conjunction with the automated processes. The findings indicate that the vacuum system based on dual ejectors can achieve rapid response and low air consumption. This paper contributes to advancing the research on energy-saving applications of ejectors. • Single-factor analysis is used to study the effect of each parameter. • Multi-factor analysis is used to optimize the ejector. • The recommended values of the ejector for different applications are given. • The findings can help the dual-ejector vacuum system achieve energy saving. [ABSTRACT FROM AUTHOR]

Contemporary applications leverage machine learning models to optimize performance, often necessitating data transmission to a remote server for training. However, this approach entails significant resource consumption. A privacy concern arises, which Federated Learning addresses through a cyclical process involving in-device training (local model update) and subsequent reporting to the server for aggregation (global model update). In each iteration of this cycle, termed a communication round, a client selection component determines participant devices contributing to global model enhancement. However, existing literature inadequately addresses scenarios where optimized energy consumption is imperative. This paper introduces an Energy Saving Client Selection (ESCS) mechanism, considering decision criteria such as battery level, training time capacity, and network quality. As a pertinent use case, classification scenarios are utilized to compare the performance of ESCS against other state-of-the-art approaches. The findings reveal that ESCS effectively conserves energy while maintaining optimal performance. This research contributes to the ongoing discourse on energy-efficient client selection strategies within the domain of Federated Learning. [ABSTRACT FROM AUTHOR]

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a co-evolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles. • The study proposes a novel two-stage adaptive co-evolutionary clustering algorithm. • The proposed method shows superior advantages in energy conservation and emission reduction. • The impact of the vehicle path decision on the charging station location plans is considered. • Analyzing the impact of network scale, traffic volume, and grid capacity on the model. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has been widely recognized as a direct, efficient, and cost-effective approach to advancing energy conservation, reducing emissions, and achieving sustainable development. While there are numerous and effective methods to enhance energy efficiency in a typical steel manufacturing system, they are relatively scattered and independent, lacking a systematic review for multi-level, comprehensive and scientific evaluation and improvement of energy efficiency within the steel industry. To fill this gap, this work initially presents an overview of the component units and energy networks within steel production system. Subsequently, a comprehensive review and analysis are conducted from multiple perspectives, such as energy evaluation, diagnosis, benchmarking analysis, optimization, and energy-saving measures. Each part meticulously selects the literature through content evaluation analysis to discuss methods, technologies, and models for improving energy efficiency, along with potential challenges that need to be overcome for future advancements. Furthermore, the paper examines the diverse challenges encountered by steel manufacturing in realizing energy efficiency improvements and exploring energy-saving potential. It also underscores the corresponding future developmental direction, including the construction of multi-level evaluation systems, implementation of intelligent energy efficiency improvement methods and technologies, and adoption of intelligent energy systems crucial for intelligent analysis and manufacturing in steel sector. • Energy efficiency improvement methods within the steel production sector are comprehensively reviewed. • Multi-level energy efficiency evaluation methods for the steel production are summarized. • Advances in energy efficiency diagnosis, benchmarking analysis, optimization and energy-saving technologies are summarized. • Challenges and prospects for energy efficiency improvement in steel sector are discussed. [ABSTRACT FROM AUTHOR]

To effectively optimize household energy management, enhance user comfort, and ensure the stability of the power grid, this paper directs its focus towards household equipment and presents inter-household and indoor energy scheduling algorithms. Grounded in a two-dimensional dynamic device priority framework and predictive device operation modeling, this study demonstrates that the utilization of scheduling algorithms can avert grid overload scenarios during periods of low electricity prices. This results in more consistent hourly electricity consumption and a substantial peak-to-average ratio reduction of up to 44.86 %. Additionally, the application of the continuous wavelet neural network algorithm proves to be proficient in accurately forecasting home device operations. When the time was 17.5 h, the actual energy consumption of the water heater was 7.12 kWh, which was 0.78 kWh more than the predicted energy consumption. Utilizing scheduling algorithms, as opposed to not employing them, led to notable reductions in electricity expenses, demonstrating a cost savings rate of 15.32 % on the third day. Additionally, it facilitated decreased usage of the power grid. When the time was 18 o'clock, the power consumption of the grid using scheduling algorithms was 0.24 kWh, which was 0.25 kWh lower than that without scheduling algorithms. Following the implementation of the scheduling algorithm, minimal deviation in device startup times was observed during periods of high comfort, with offsets ranging from 0 h. The research methods employed demonstrate efficacy in orchestrating energy distribution among households and indoor spaces, thereby striking a harmonious balance between energy conservation and user comfort. [ABSTRACT FROM AUTHOR]

Under "double carbon" policies, the shift towards bus electrification is unavoidable but is hindered by operational challenges due to electric buses' limited range. These challenges are intensified by heterogenous bus fleets, inadequate charging facilities, and depot limitations. This paper introduces a pragmatic approach to address the multi-depot electric bus scheduling problem, comprehensively addressing all aforementioned complexities. A tailored adaptive large neighborhood search (ALNS) is designed to solve the problem, wherein several ingenious accelerating techniques are integrated to generate a high-quality solution within an acceptance time frame, including: 1) relaxation of hard constraints to expedite iterations; 2) specialized operators designed to enhance the diversity of neighborhood spaces; 3) the meticulous setting of operator scores to prioritize operators with higher efficiency. A small-scale case study is conducted to compare the tailored ALNS with the commercial solver. The results substantiate the superior performance of the proposed algorithm and the significant role of these accelerating techniques. Furthermore, the algorithm presents its practical applicability in large-scale scenarios. The findings reveal that the proposed approach reduces the fleet size by 35 vehicles and concurrently achieves a significant 13.61 % reduction in electricity consumption, which provides a holistic solution to real-world operational challenges and offers recommendations for operators. • A holistic solution designed for the multi-depot electric bus scheduling problem. • Considering limitations of charging facilities and depot capacity constraints. • An adaptive large neighborhood search algorithm embedded with ingenious techniques. • Real cases verify the method enhances vehicle utilization and energy efficiency. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of Sustainable Aviation Fuel (SAF) to achieve the imperatives of energy conservation and emission reduction. This paper compares the combustion characteristics of diesel, RP-3/n-butanol blends in terms of heat release rate, in-cylinder pressure, temperature and fuel economy in a compression ignition engine, taking into account the advantages of lightweight structure, low fuel consumption and wide range of applications of aviation piston engines. The results certified the viability of applying RP-3 and RP-3/n-butanol blends in diesel engines. Adding n-butanol prolonged the ignition delay and shorten the combustion duration. Moreover, the peak of the pressure, temperature and apparent heat release rate (AHRR) increased. In addition, the blended fuel showed obvious diffusion combustion characteristics at high load. With the n-butanol increased, the combustion instability of the blended fuels decreased significantly, at low load. At n = 2400r/min, pme = 0.53 MPa, compared with diesel, the maximum heat release rate (MHRR) of R70B30 increased by 76.6 %, improving the combustion characteristics of the blended fuel. Moreover, the equivalent specific fuel consumption (ESFC) of RP-3 is 5.56–14.83 % lower than that of diesel, and the effective thermal efficiency (ETE) of RP-3 is 2.8–13.46 % higher than that of diesel under most working conditions. • The n-butanol/RP-3 blended fuels can replace diesel fuels in compression ignition engines. • The n-butanol improves the combustion difficulties at low load. • The blended fuel shows obvious diffusion combustion characteristics at high load. • Using n-butanol as blended fuel can shorten the combustion duration. [ABSTRACT FROM AUTHOR]

In this paper, we propose and analyze a new fourth-order compact finite difference scheme for solving the quantum Zakharov system (QZS). The new scheme perfectly inherits the mass and energy conservation possessed by the QZS, while the energy preserved by the existing schemes expressed by two-level's solution at each time step. By using the energy method to analyze an equivalent form of the difference scheme, without any requirement on the grid ratio, we establish rigorously the optimal error estimate of the proposed scheme in the energy norm. The accuracy is shown to be fourth order in space and second order in time. Numerical examples are presented to verify the theoretical results and show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The dairy industry requires substantial energy resources at all stages of production and supply to meet consumer needs in terms of quantity, quality and food safety. The expected future climate change effects will cause serious uncertainty to the dairy industry. Adapting to these upcoming conditions is a challenge and one that is compounded by the continuous increase in food demand, as a result of global population growth. Predictably, under current conditions, this situation might lead to a significant increase in the energy requirements of the dairy industry. Therefore, there is a clear need to mitigate energy use through enhanced energy conservation, waste reduction and waste management. This review paper presents and discusses alternative dairy operations and mitigation strategies that have the potential to lead the dairy industry towards net-zero carbon emissions. Further, the focus of this work turns to supply chain energy modelling (SCEM) as means to mitigate energy use, while relevant work in the literature is reviewed. Supply chain energy models can provide a complete overview of the energy demand and the energy mix of a dairy supply chain. Additionally, they can highlight the most energy consuming processes and allow the evaluation of alternative energy-saving operations that can lead towards the net-zero carbon target. Overall, the development or use of computational tools for simulating the energy demand in the industry has strong potential for improving sustainability across the dairy supply chain. • Insight on climate change effects on the dairy supply chain and impact on energy demand. • Understanding the relevance and importance of the "net-zero" carbon emissions target in the dairy supply chain. • Identification of novel energy mitigation strategies across the dairy supply chain. • Importance of developing supply chain energy models (SCEM) to address energy mitigation in the dairy sector. • Evaluation of modelling techniques to develop or deliver supply chain energy models. [ABSTRACT FROM AUTHOR]

Highlights • A large-scale of 15,488 EnergyPlus simulations for commercial buildings in the U.S. • Nationwide energy savings for a single and combinations of multiple ECMs. • Nationwide energy savings for the ECM of fault detection and diagnostics. Abstract Buildings consumed 40% of the energy in the United States, which is more than any other sectors of the U.S. economy, including transportation and industry. The majority of this energy was used for lighting, space heating, cooling, ventilation, and water heating in the buildings. It is well known that the carbon emission is proportional to the energy consumption. Enhancing building efficiency becomes one of the easiest, most immediate and most cost-effective ways to reduce carbon emissions. This paper focuses on the simulation-based nationwide energy savings analysis of different energy conservation measures (ECMs) to enhance building energy performance. The study includes 44 different ECMs, which were simulated in 16 different climate zones for 11 different commercial building types in the U.S. Simulations were conducted using a whole building simulation program, EnergyPlus. Commercial prototype building models from the U.S. Department of Energy [1] were used as the baselines. Model input files were automatically modified to incorporate with 44 different ECMs using scripts. The baselines were based on American Society of Heating, Refrigeration, and Air-conditioning Engineers (ASHRAE) [2] Standard 90.1 and International Energy Conservation Code (IECC). A total of 15,488 simulations were conducted to study the nationwide energy savings potentials. The details of these 44 proposed ECMs and their corresponding nationwide saving potentials are presented and discussed in this paper. [ABSTRACT FROM AUTHOR]

Abstract Water pumping systems are widely used in industrial and civil applications. During their operational life, they consume energy and materials (mainly for installation and replacement of components). The aim of this paper is to optimize the pump operations in order to save energy and enlarge the operational life of the pumps and their components. This paper addresses the multi-period (e.g. 24-hours time horizon) optimization of pumping systems. To this end, we have developed a simulation-based optimization approach including novel relevant technical features of a generic but realistic pumping system, namely cavitation and overflow. The proposed multi-period optimization differs further from the classic static optimization (i.e. at the design stage), proposing a new dynamic approach in which pump activation is steered dynamically for an optimal management of the variability of water inflow. Furthermore, the problem includes constraints on the number of pump activations allowed to reduce material strain. The performances of different meta-heuristic optimization algorithms (e.g. genetic algorithm, simulated annealing and particle swarm optimization) for solving the problem are compared. The numerical results show that energy savings are possible with the dynamic approach and that particle swarm optimization and simulated annealing algorithms provide the most suitable solutions for this problem. Highlights • Three algorithms are compared to minimize energy pump consumption. • The pump model includes energy, cavitation, overow and activation frequency. • Static and dynamic optimizations are investigated. • The dynamic optimization can reduce the energy consumption by 6.5%. [ABSTRACT FROM AUTHOR]

Abstract The extensive use of energy-saving appliances can promote household energy conservation, which, to a certain extent, can help to alleviate haze pollution. Based on the Theory of Planned Behavior and Normative Activation Model, a research framework is developed in this paper to explore residents' intention to purchase energy-saving appliances under the influence of severe haze pollution. This paper takes the residents in Xuzhou as the object of survey. Then, the structural equation model and Bootstrap method are used to analyze the valid measurement questionnaires. The results demonstrate that residents had a very strong intention to purchase energy-saving appliances under the influence of haze pollution from the perspective of self-interest and altruism, respectively. Attitude, subjective norm, perceived behavior control and personal norm are the key factors influencing residents' purchasing intention of energy-saving appliances under frequent haze pollution, of which attitude is the most powerful influence. In addition, ascription of responsibility is a partial mediator between the relationship of awareness of consequences and personal norm, and together with awareness of consequences can positively influence personal norm. Finally, based on the empirical results, this paper proposes measures to guide consumers to buy energy-saving appliances. [ABSTRACT FROM AUTHOR]

Highlights • A pre and post renovation energy use analysis of almost 90,000 renovated dwellings. • The (p)rebound effects explain only partly disappointing energy renovation results. • Effectiveness of renovations are dependent on state of building prior to renovation. • Effectiveness of renovations are dependent on type of occupant. • Deep renovations often save less energy than expected but are the most effective. Abstract Energy renovations often result in lower energy savings than expected. Therefore, in this study we investigate nearly 90,000 renovated dwellings in the Netherlands with pre and post renovation data of actual and calculated energy consumption. One of the main additions of this paper, compared to previous studies on thermal renovation, is that it only takes dwellings into account with the same occupants before and after renovation, using a large longitudinal dataset. Overall this paper shows new insights towards the influence of the energy efficiency state of a building prior to energy renovation, the type of building, the number of occupants, the income level of the occupants and the occupancy time on the actual energy savings, the energy saving gap and on the probability of lower energy savings than expected. We also investigate if the influence is different per type of thermal renovation measure. Some of the findings are: it is impossible to conclude which single thermal renovation measure is the most effective because this is dependent on the energy efficiency of the building prior to the energy renovation, type of building, income level and occupancy; occupants with a high income save more energy than occupants with low income; dwellings with employed occupants benefit more from improved building installations than dwellings occupied by unemployed occupants; The prebound and rebound effects are only part of the explanations for lower than expected energy savings; Deep renovations result more often in lower than expected energy savings than single renovation measures but nevertheless they result in the highest average energy saving compared to other thermal renovation measures. The results could be used for more realistic expectations of the energy reduction achieved by thermal renovations, which is important for (amongst others) policy makers, clients and contractors who make use of energy performance contracting, home owners, landlords and (social) housing associations and as a starting point to improve the energy calculation method. [ABSTRACT FROM AUTHOR]

Abstract Software systems have a strong impact on the energy consumption of the hardware they use. This is especially important for cyber-physical systems where power consumption strongly influences the battery life. For this reason, software developers should be more aware of the energy consumed by their systems. Moreover, software systems should be developed to adapt their behavior to minimize the energy consumed during their execution. This can be done by monitoring the usage context of the system and having runtime support to react to those changes that impact the energy footprint negatively. Although both the hardware and the software parts of cyber-physical systems can be adapted to reduce its energy consumption, this paper focuses on software adaptation. Concretely, the paper illustrates how to address the problem of developing context-aware energy-efficient applications using a Green Eco-Assistant that makes use of advanced software engineering methods, such as Dynamic Software Product Lines and Separation of Concerns. The main steps of our approach are illustrated by applying them to a cyber-physical system case study. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the co-movement and dependence structure between China's emissions allowances (CEA) and wholesale diesel (gasoline) markets using a GARCH-copula with a generalized error distribution (GED). Chinese emissions allowances prices exhibit significantly extreme co-movement with wholesale diesel and gasoline prices using GARCH and TARCH models based on a GED. The empirical results confirm that different markets except Hubei's emissions-gasoline market exhibit a greater divergence of asymmetric tail dependence structure between emissions allowances and wholesale diesel (gasoline) markets. Beijing's emissions allowances prices show an asymmetric mixture of right-tail and left-tail dependence structures with wholesale diesel and gasoline prices in northern China, implying stronger right-tail dependence. Shanghai and Guangdong's emissions allowances prices indicate a significant right-tail dependence structure with wholesale diesel and gasoline prices in East and South China. Hubei's emissions allowances prices exhibit an asymmetric mixture of right-tail and left-tail dependence structures with wholesale diesel prices, implying stronger left-tail dependence and a significantly symmetric dependence structure with wholesale gasoline prices in central China. Highlights • This paper presents the mixed copula-GARCH model based on GED. • We examine the co-movement between emissions allowances and fuel markets. • Different markets exhibit significant and asymmetric tail dependence structure. • Most of emissions-fuel markets exhibit a positive dependence. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a novel distributed cooperative control-based online air balancing (DCC-AB) method for the existing ventilation duct systems. Inspired by the application of distributed cooperative control (DCC) in the field of network communication, the DCC is first introduced into HVAC systems to solve the air balancing problem. Based upon the consensus algorithm, the proposed DCC-AB method realizes air flow balance via a prescribed information exchange among the agents in the duct system. The proposed method also incorporates an additional adjustable term that reflects the total power consumption to optimize the energy efficiency. Compared with the existing air balancing methods, the DCC-AB method offers the following advantages: 1.) It is an online method that can be performed during normal operation of the ventilation system without service interruptions. 2.) The distributed control strategy in this method eliminates the necessity of a centralized supervisory control. 3.) It is a model-free method that requires no prior knowledge on the system topology and duct parameters and is therefore easy to apply in practical use. The operation principle and parametric analysis of the DCC-AB method are carefully discussed in this paper. Experiments have also verified the effectiveness of the proposed DCC-AB method. The experimental results demonstrate that the proposed method applies over a large range of design flow requirements and yields robustness against small sensor noise. A rapid and stable convergence is guaranteed among all tested cases and the maximum absolute percentage error can achieve 2.3%. Highlights • The proposed method can be performed during normal operation of the ventilation system without service interruptions. • The proposed method requires no prior knowledge on the system topology and duct parameters. • The proposed method optimizes the energy efficiency by guaranteeing at least one damper to be fully open. • The proposed method ensures rapid convergence and stability under different design flow conditions. [ABSTRACT FROM AUTHOR]

Nowadays, one of the strategies that are being applied in DC railway networks to improve energy efficiency is the installation of Reversible Substations or Energy Storage Systems. The assessment of their impact on the network consumption is typically based on simulation. The accuracy of the results provided by the simulations relies on the accuracy of the models used. This paper focuses on the traffic model used in electric railway simulators. A preliminary literature review has shown that, in most cases, the traffic models of electric railway simulators involve too many simplifications that can lead to non-accurate results. This lack of accuracy can have a great impact in the assessment of the infrastructure improvements, reason why a more accurate traffic model is proposed. The proposed traffic model includes different headways, different time shifts at terminal stations, non-constant dwell times, different train speed profiles and a specific module of the traffic regulation system in real time. The energy saving resulting from the installation of a Reversible Substation obtained with both models –the one used in the literature and the one proposed in this paper– has been compared to illustrate the risk of using a too simplified traffic model. The comparison shows that the simplified model used in the literature produces non-accurate enough estimations of the energy saving. [ABSTRACT FROM AUTHOR]

Energy extraction from renewable sources like hydrokinetic energy from the flowing water of rivers and canals is one of the affordable and clean energy generation technique. The current work is carried out towards performance enhancement of the Darrieus hydrokinetic turbine for open water channel utilization application. The investigation has been done using the flat and concave shape of blocking plate. Both blocking plates have been analyzed for different sizes as well as location at the upstream side of the turbine. The obtained results are reported after considering appropriate velocity correction. The blocking plates with projected width of 76 mm, 89 mm, 101 mm, 127 mm, 152 mm, and 203 mm are selected for the investigation. It is observed that 101 mm (Width Ratio , WR =0.36) with concave blocking plate provides maximum C p of 0.245 at 0.99 TSR. Also, the optimum region is found between 0.143 and 0.286 Distance Ratio (DR) from the top corner of the acrylic plate. The concave blocking plate enhance the performance more compared to flat blocking plate. The conclusion is made that, the performance with flat blocking plate with optimized width and location improve the power coefficient by 10.92% and that of concave blocking plate by 18.45%. • Energy extraction using Darrieus hydrokinetic turbine is one of the affordable and clean energy generation techniques. • The performance of the Darrieus water turbine has been compared with a bare rotor and with using two different types of blocking plates. • The concave and flat blocking plates have been experimentally analyzed, and its effect has been explored in the paper. • The different sizes of concave and flat blocking plates were placed at the upstream side of the Darrieus turbine to enhance the performance. • The paper also includes the optimum region of the optimized shape concave and flat blocking plates placed at the upstream location of the rotor. [ABSTRACT FROM AUTHOR]

The construction industry plays a vital role in global economic development and is also an important industrial sector affecting global energy consumption. Energy conservation is an important guarantee for global sustainable development. Based on the perspective of embodied energy intensity of the global construction industry, this paper analyzes the reasons behind its high energy consumption. This paper uses a multi-regional structural path analysis model to explore the path of embodied energy intensity (EEI) in the global construction industry. The results show that the 2nd layer is the main path for the EEI in the global construction industry. In contrast, the higher layer plays an increasingly critical role in the EEI of the global construction industry. Its contribution increased by 49.27% during the study period. The construction material supply is the main source in the 2nd layer, and the coke and petroleum product manufacturing and power and gas supply are the main sources in the higher layer. Among the factors driving the inter-temporal change of EEI in the global construction industry, the sector energy intensity effect has a promoting effect on the decline of the EEI in all layers, while the final demand effect and input-output structure effect inhibit the EEI decline. • The 2nd layer is the main path for the EEI in the global construction industry. • The higher layer plays an increasingly critical role in the EEI of the global construction industry. • The construction material supply is the main source in the 2nd layer. • The coke and petroleum product manufacturing and power and gas supply are the main sources in the higher layer. • The final demand effect and input-output structure effect inhibit the EEI decline. [ABSTRACT FROM AUTHOR]

Energy conservation and preventive maintenance of equipment require the ability to accurately predict future trends in shop floor power consumption to keep track of equipment operation and abnormalities. Due to the non-linearity and uncertainty of the workshop electrical energy consumption data, it is very difficult to establish an accurate energy consumption prediction model. For the above problems, an energy consumption prediction model based on Prophet-EEMD-LSTM was proposed. To identify the period features in the energy consumption time series data, the Prophet algorithm was introduced to identify and extract the period and trend features of the energy consumption data. Then for the feature of the uncertainty of the remaining data, this paper used the ensemble empirical mode decomposition (EEMD) method to decompose the data to form the components with specific modalities. Finally, all the extracted feature data were put into the input layer of the Long short-term memory (LSTM), and the final energy consumption values were predicted using the excellent prediction performance of the LSTM on time-series data. The experimental results showed that the model proposed in this paper has high prediction performance for energy consumption data. • Energy consumption prediction models guide energy saving and predictive maintenance in paint workshop. • The model uses only energy consumption data for predictions, eliminating the need for other data. • The complexity of the data is reduced by Prophet and EEMD, and the LSTM captures the temporal characteristics of the data. • The Prophet-EEMD-LSTM model shows higher performance with an average R2 value of 0.94006. [ABSTRACT FROM AUTHOR]

A collection of mobile devices is defined as a Mobile Ad-hoc Network (MANET). Without any prior management these devices can communicate and synchronize with one another. The state of the concerned channel determines how much life energy can be increased. It also extends the node's lifetime while it waits for the control messages to be completed. The nodes' lifespan and packet flow quality are harmed in the current system due to unanticipated energy conservation strategies. It causes post-network collapse due to an energy throughput trade off. This failure causes an increase in overhead and a decrease in Time-to-Live (TTL). In addition to proposing a novel performance modeling in Volunteered Computing MANET and tactile internet, this paper discusses an effective buffer management mechanism. The best execution the nodes can achieve with fractional data is depicted for utilities in general. A switch state promoting Optimized mutual MAC (OMAC) process was presented in this study for node conserving energy and the optimal utilization of accessible nodes before their resource drain to link the space between network efficiency conservation based on minimal overhead. As proof of the proposed solution, simulation results are provided. Compared the simulation outcomes with the existing system using the parameters like latency, quantity, node availability & energy consumption. [ABSTRACT FROM AUTHOR]

Energy conservation in a highly resource restricted environment of Wireless Sensor Networks (WSNs) is a crucial task. This issue can be efficiently tackled by adopting the mechanism of clustering in sensor networks. The primary objective of any of the designed clustering protocols for WSNs is to obtain energy efficiency and to lengthen the network life duration. This achievement is made through the election of a single deserving node as a head node called cluster head. This paper suggested an evolutionary approach based clustering protocol named Modified-Invasive Weed Optimization Based Clustering Algorithm (M-IWOCA) for WSNs. M-IWOCA focuses on the selection of fittest node as a cluster head to enhance the network's lifetime as well as to address the issue of energy conservation in sensor networks. A fuzzy inference model is designed to evaluate the fitness of each node in the network. Simulation results for M-IWOCA depict significant reduction in dead nodes per round and results in minimizing the energy utilization of the sensors. Moreover, M-IWOCA enhances the network stability period by 45% in comparison to Artificial Bee Colony (ABC) protocol and by 18% in comparison to Quantum Artificial Bee Colony (QABC) protocol. [ABSTRACT FROM AUTHOR]

As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization. [Display omitted] • Energy consumed by motion resistances (ECMR) of high-speed trains (HSTs) is studied. • A dynamic model of the HST is developed to characterize ECMR under various scenarios. • A strong correlation between speed and track properties on ECMR is observed. • A unified framework is built to optimize the cruising speed for energy conservation. • Up to 27.85% of ECMR can be reduced through the cruising speed optimization. [ABSTRACT FROM AUTHOR]

The food industry is one of the world's largest contributors to carbon emissions, due to energy consumption throughout the food life cycle. This paper is focused on the residential consumption phase of the food life cycle assessment (LCA), i.e., energy consumption during home cooking. Specifically, while much effort has been placed on improving appliance energy efficiency, appliance models used in various applications, including the food LCA, are not updated regularly. This process is hindered by the fact that the cooking appliance models are either very cumbersome, requiring knowledge of parameters which are difficult to obtain or dependent on manufacturers' data which do not always reflect variable cooking behaviour of the general public. This paper proposes a methodology for generating accurate appliance models from energy consumption data, obtained by smart meters that are becoming widely available worldwide, without detailed knowledge of additional parameters such as food being prepared, mass of food, etc. Furthermore, the proposed models, due to the nature of smart meter data, are built incorporating actual usage patterns reflecting specific cooking practice. We validate our results from large, geographically spread energy datasets and demonstrate, as a case study, the impact of up-to-date models in the consumption phase of food LCA. [ABSTRACT FROM AUTHOR]

This paper introduces a growth model that considers the indicator of economic complexity as a measure of capabilities for exporting the high value-added (sophisticated) products. Empirically, the paper analyzes the effects of the renewable and the non-renewable energy consumption on the economic growth in the panel data of 29 Organization for Economic Co-operation and Development (OECD) countries for the period from 1990 to 2013. For this purpose, the paper considers the panel autoregressive distributed lag (ARDL) and the panel quantile regression (PQR) estimations. The paper finds that not only the economic complexity, but also both the non-renewable and the renewable energy consumption are positively associated with a higher rate of economic growth. [ABSTRACT FROM AUTHOR]

This paper explores the salient factors driving the changes in energy consumption in China's nonferrous metal industry during the 2000–2014 period. We employ the logarithmic mean Divisia index (LMDI) method to decompose the change in energy consumption into the energy structure effect (Δ E ES ), the energy intensity effect (Δ E EI ), the industrial structure effect (Δ E S ), the labour productivity effect (Δ E G ) and the industrial scale effect (Δ E L ). The main results revealed the following: (1) from 2000 to 2014, China's nonferrous metal industry's energy consumption increased by approximately 69.08 million tons of coal equivalent (tce); (2) Δ E G increased energy consumption in all years and was the largest contributor to the increase in energy consumption (followed by Δ E L ), whereas Δ E EI was the dominant factor in reducing energy consumption over the same period, accounting for 104.07% of the change in the absolute value of total energy consumption; and (3) Δ E ES and Δ E S contributed 0.24% and 1.45% to the change, respectively. At present, the decline in Δ E EI cannot completely offset the increases resulting from the other four effects. This paper then provides several policy recommendations based on these results. [ABSTRACT FROM AUTHOR]

With the rapid development of subway construction in developing countries like China, energy conservation in subway stations is gaining popularity within society. Generally, ventilation and air-conditioning play important roles in the total energy consumption. As a major feature of subway stations, the train-induced airflow (TIA) driven by moving trains has been recognized for years, having a great influence on indoor environment and energy consumption. However, there is a lack of case study on real amount of train-induced airflow inside stations. In order to get extensive knowledge of the real situation, this paper proposes a set of simplified methods to measure and calculate train-induced airflow rates. Using these methods, field tests were conducted in several typical underground subway stations in China during the spring of 2016. This paper presents the basic situation of train-induced airflow in these stations and the influence of different ventilation modes. Based on the fact that there exists unneglectable amount of train-induced airflow, and considering the demand-supply analysis of fresh air, the potential for high levels of energy savings could be identified in the mechanical ventilation systems of these stations. These new findings are of great benefit for energy-efficient operation of existing stations. [ABSTRACT FROM AUTHOR]

The high-energy consumption of the architectural, engineering and construction (AEC) industry and associated environmental pollution have become a global challenge, and governments at different levels in China have been dedicated to improving the industry's sustainability. However, although the concept of green building (GB) has been growing rapidly, the primarily emphasis has been on energy-saving design with little attention paid to sustainable post-occupancy operations, which is hindering further development. To address this fundamental issue it is necessary to evaluate the post-occupancy performance of GB and, given China's current circumstances, one that involves the participation of all stakeholders to avoid being dominated by construction professionals. However, such participatory evaluations are currently very limited and perfunctory in the country, usually involving simply informing or placating the stakeholders. In response, this paper develops a comprehensive quantitative method to analyze stakeholder impact during GB post-occupancy evaluation (POE). This enables the various stakeholder groups to be prioritized in terms of their influence levels and hence contributes to maximizing overall stakeholder satisfaction by improving the efficiency and effectiveness of the evaluation. The findings of the paper are expected to help clients and design teams improve their building designs by integrating the views of stakeholders through the POE in order to realize the true spirit of GB development. [ABSTRACT FROM AUTHOR]

Under the policy of energy saving and emission reduction, technological innovation has become the key power-driver to improve energy utilization efficiency. By using the panel data of 21 industrial segments between 2000 and 2015, this paper investigates the driving effect of technological innovation on energy-utilization efficiency in strategic emerging industries and analyzes the critical factors influencing the driving effect of technological innovation by the stochastic frontier analysis method. The empirical result shows that technological innovation between 2000 and 2015 has had a significant positive driving effect on the energy utilization efficiency of strategic emerging industries and exhibits a development trend of year-by-year steady-state growth on the whole. The greater the value of the development expenditure of new green products and the investment in environmental pollution treatment, the greater the technological efficiency; the greater the value of the transformation expenditure of energy-saving technology, the greater the technological inefficiency. Finally, the paper proposes the countermeasures and suggestions for the driving effect of technological innovation on energy utilization efficiency in strategic emerging industries. An empirical study is made to analyze the driving effect of technological innovation on energy utilization efficiency, the positive and negative factors of technological innovation are forgotten. Strategic emerging industries can select optimal innovation behavior to improve quality and efficiency of energy utilization based on the results. [ABSTRACT FROM AUTHOR]