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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: 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]

• 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]

Reliability is a crucial index of the system, and many safety-critical applications have reliability requirements and deadline constraints. In addition, in order to protect the environment and reduce system operating costs, it is necessary to minimize energy consumption as much as possible. This paper considers parallel applications on heterogeneous distributed systems and proposes two algorithms to minimize energy consumption for meeting the deadline and satisfying the reliability requirement of the applications. The first algorithm is called minimizing scheduling length while satisfying the reliability requirement (MSLSRR). It first transforms the reliability requirement of the application into the reliability requirement of the task and then assigns the task to the processor with the earliest finish time. Since the reliability generated by MSLSRR is often higher than the reliability requirement of the application, and the scheduling length is also less than the deadline, an algorithm called improving energy efficiency (IEE) is designed, which redefined the minimum reliability requirement for the task and applied dynamic voltage and frequency scaling (DVFS) technique for energy conservation. The proposed algorithms are compared with existing algorithms by using real parallel applications. Experimental results demonstrate that the proposed algorithms consume the least energy. [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]

Remanufacturing attains much attention from both industrial and academic sectors due to its beneficial roles in energy conservation and environment protection, where disassembly is a crucial part. To reach the comprehensive sustainability and global optimization of disassembling multiple end-of-life products, this paper suggests a multi-objective multi-product disassembly line balancing problem with considering disassembly profit, energy consumption and noise pollution simultaneously. According to the natures of the problem under consideration, a multi-objective integer programming model is constructed. Its goals are to reach maximum disassembly profit and realize minimum energy consumption while observing noise pollution requirements and resource constraints. Accordingly, a multi-objective group teaching optimization algorithm is specially devised. In it, rank and crowding distance methods are employed to partition the population into two groups. Moreover, precedence preserving crossover and mutation methods are severally used on the two groups to realize the teacher phase. Furthermore, to achieve the student phase, an adaptive local search method is applied to refine solutions in an external archive, and thus its exploitation ability is enhanced. By executing contrast experiments between the devised approach and its powerful competitors on a set of real-world test instances, the experimental results validate that it has highly-adaptive and well-superior performance in tackling the problem of concern. [Display omitted] [ABSTRACT FROM AUTHOR]

Energy quota trading is critical to both energy conservation and the achievement of dual-carbon goals. However, little remains to be done on total factor productivity and economic potential for Chinese industry. The purpose of this paper is to counterfactually model and explore the effects of energy quota trading on total factor productivity and economic potential in industrial sector. We propose a command-controlled DEA model and an energy quota trading DEA model through non-parametric linear programming based on two policy strategies of energy consumption control. We then assess total factor productivity and its growth sources for 35 two-digit industrial sector in China, as well as examine economic potential and optimal path forecasts for carbon peaking. The results show that total factor productivity and economic potential for energy quota trading are higher than those for command-controlled energy policy. Energy-saving effect can be achieved for energy quota trading because the total energy input is lower. Additionally, the sources of productivity growth differ in timing. The average economic potential and total factor productivity of different industries differ due to the heterogeneity of the production conditions and scale of development of individual market players. Moreover, the catch-up emission reduction path with an increase of 8% in output and 4.76% in energy consumption is the optimal carbon peaking path under energy quota trading. Our findings shed valuable light on China's efforts to expand energy quota trading for high-quality development in the context of carbon peaking. • Propose a command-controlled DEA model and an energy quota trading DEA model. • Assess TFP and its growth sources for industrial sector in China. • Compare the impact of different energy policies on economic potential and TFP. • Estimate the energy saving and emission reduction potential of energy quota trading. • Examine the optimal energy-saving and emission-reduction path forecasts for carbon peaking.. [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]

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach. [Display omitted] [ABSTRACT FROM AUTHOR]

Utilizing solar energy is an effective method to decrease fossil energy consumption in marine field. Horizontally installed deck photovoltaic (PV) systems have been widely investigated to reduce navigation energy consumption on ocean-going ships. However, energy saving rate is unsatisfactory because of the low energy density of solar energy and the shadow caused by ship body. As for large amount of offshore small ships, it's even harder to utilize the existing technology since deck space is limited. In this paper, semi-transparent CdTe PV window system is proposed for offshore passenger ships without enough deck spaces but with large window area around ship cabin. Multidimensional PV windows can increase PV area and reduce heat gain inside the cabin, shadow effect caused by ship body is also avoided. Energy saving can be both achieved by electrical generation actively and cooling load reduction passively. Dynamic mathematical models of multidimensional PV window under moored and sailing conditions are established. Simulation results indicate that the daily electrical generation and average electrical efficiency are 0.74 kWh & 14.65% in moored condition and 0.83 kWh & 16.24% in sailing condition. Navigation leads to higher received solar radiation (18.2%), higher electrical generation (12.1%) and higher electrical efficiency (10.85%). Electricity created by PV window can support 6 h' continuous operation for auxiliary system on board. With PV windows, the average air temperature in cabin is decreased by 6.5 °C compared with ordinary windows. The average heat fluxes through four directions of PV windows are decreased by 75.44%, 73.64%, 47.52% and 75.33% compared with ordinary windows. • Ship integrated semi-transparent CdTe PV window is proposed at the first time. • The variation of solar radiation received by multidimensional PV window with time and sailing route is researched. • Active (electricity generation) and passive (cooling load reduction) energy conservation are both achieved by PV window. • Navigation leads to higher electrical generation and electrical efficiency than moored condition. • Average and maximum cabin air temperatures are decreased by 6.5 °C and 11.0 °C. [ABSTRACT FROM AUTHOR]

• Accordingly, a flexible traction power supply system (FTPSS) composed of a TT and multi-port power hub and its coordinated control strategy are proposed for VU compensation and energy conservation in this paper. The main contributions can be summarized as follows: • Different from previous research, a power hub is installed in the TPSS to achieve power flow control among the utility grid, traction network, and DC grid. The FTPPS with the power hub can enable VU compensation for the utility grid, which also offers a friendly interface with distributed PV and ESS for on-site access. • A three-port VU compensation method based on active power flow control and reactive power injection is presented, which divides the system demand compensation capacity and reduces the performance requirements for power electronics, compared with traditional compensation methods. • A coordinated control strategy with a hierarchical structure is designed for FTPSS. The control strategy enables real-time power management to enhance energy utilization and mitigate VU in the system layer and coordinates multiple converters to realize the multi-function operation of the system in the converter layer. To mitigate voltage unbalance (VU) and eliminate the neutral sections while reducing the energy consumption of railways, a flexible traction power supply system (FTPSS) with the power hub is proposed. Different from conventional schemes, the proposed system provides a friendly interface to realize the on-site access of distributed photovoltaic (PV) generation along the railway line and concentrated integration of energy storage systems. The power flow is centrally managed by the power hub to reduce energy consumption and the proposed three-port VU compensation method is coupled with the power hub to suppress negative sequence current caused by trains. Considering the volatility of PV generation and traction loads, four operation modes including grid-connected, valley filling, VU compensation, and peak clipping mode, are proposed to dynamically regulate the operation of FTPSS. Moreover, a coordinated control strategy with a hierarchical structure is developed to enable the operation modes. The strategy is implemented with a global controller and multiple local controllers, which can realize real-time power management and flexible transition among different modes. Finally, different simulation and experimental case studies based on the field data are carried out to comprehensively verify the feasibility and effectiveness of the proposed FTPSS and control strategy. [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]

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]

This paper introduces a new energy management (EM) strategy for series hybrid electric vehicles (HEVs). Series HEVs operate in charge-depletion mode and then switch to the charge-sustaining mode in which the battery state of charge (SOC) is maintained within a certain range. The proposed EM strategy in this paper is a form of adaptive equivalent consumption minimization strategy (ECMS) that is designed for the charge-sustaining mode. The EM strategy defines soft bounds on the battery SOC and is penalized for exceeding these bounds. But, to catch energy-saving opportunities (CESOs), the EM strategy allows SOC to exceed the soft bounds. Thus, the introduced EM strategy is named ECMS-CESO. In addition, a range for the ECMS optimal equivalent factor is proposed for series HEVs. The proposed range is used in deriving the formula for calculating the adaptive equivalent factor. The main advantage of the proposed EM strategy is that ECMS-CESO can achieve close to optimal fuel economy without the need for predicting future driver demand. Since there is no need for prediction, the intensive calculations for finding the optimal control over the prediction horizon can be eliminated. Therefore, implementation of ECMS-CESO is easily feasible for real-time applications. Experimental powertrain data is collected to develop a powertrain model for a series HEV in this study. Simulation results on several drivecycles show that, on average, the fuel economy achieved by ECMS-CESO is within 6% of the maximum fuel economy. In addition, comparing ECMS-CESO with two existing adaptive ECMSs shows up to 5% improvement in fuel economy, on average. [ABSTRACT FROM AUTHOR]

Coal mining activities have caused serious environmental problems; however, almost 30% of global primary energy demand is met using coal, making it difficult to replace in the near future. Under these circumstances, as it is not possible to completely cease coal mining activities, coal mines need to invest in cleaner production techniques to reduce environmental damage. To achieve pollution emissions and energy reductions, this paper proposes a dynamic multi-objective mixed 0–1 model based on ecological coal mining, in which environmental technological investment and production adjustments are integrated into a dynamic system to seek ecological and economic equilibrium, and emissions reductions and energy conservation are controlled using double dynamic transfer equations. Different from previous papers, this model considers the effect of production adjustments and energy conservation on the environment and the economy with the aim of achieving global optimization through integrated planning. To solve the proposed model, a hybrid algorithm with a standard and an antithetic method-based Particle Swarm Optimization (PSOs) is developed. A case study from Chaohua Colliery is presented to demonstrate the practicality and efficiency of the optimization model. The results and comparison analyses showed that as the dynamic adjustments strengthened the production and investment coordination for global optimization, integrated production and environmental investment planning provided coal mines with a superior method to solve conflicts between the ecology and the economy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

• An environment-fusion multipath routing protocol EFMRP for WSNs is proposed. • Traffic allocation and retreat mechanisms are proposed for routing maintenance. • In EFMRP, constructed routes can avoid crossing through the danger zones. • EFMRP can provide sustainable message forwarding service under harsh environments. In most cases, wireless sensor networks (WSNs) are deployed in unattended scenarios and are featured by energy sensitivity and low cost, thus making the performance of WSNs prone to the impact of external environment and internal energy. Existing routing protocols attempted to optimize the energy efficiency and routing reliability from the perspective of the network itself and failed to take into consideration the environmental impact from outside, causing them cannot make prompt reactions to the dynamic changes of the environments (e.g., wildfire). Thus, in these routing protocols the routing survivability under harsh environments is questionable. To tackle this issue, the paper proposes an environment-fusion multipath routing protocol (EFMRP) to provide sustainable message forwarding service under harsh environments. In EFMRP, routing decisions are made according to a mixed potential field in terms of depth, residual energy and environment. The basic idea of this approach is to instruct data packets to select routes with the best trade-off among latency, energy conservation and routing survivability. As the environmental field is constructed and updated using the sensing capability of WSN itself, constructed routes can avoid crossing through the danger zones to keep the paths safe. To enhance the performance of EFMRP, specific maintenance, traffic allocation and retreat mechanisms are proposed. We investigate the impact of configuration parameters and paths number on the routing performance respectively and compare EFMRP with respect to commonly used routing protocols. The experimental results show that EFMRP can obtain significant improvements in packet delivery ratio and network lifetime under harsh environments. [ABSTRACT FROM AUTHOR]

• Operating time of SMEs is estimated using a SOM and hourly demand data. • The estimated operating time is used for hourly AC demand disaggregation. • The average errors of yearly AC demand estimation are better than 10%. • Hourly disaggregation highlights season and time period of AC demand peaks. Despite the growing penetration of smart meters and energy management systems in recent years, few tools have been proposed for energy demand data analysis and visualization to promote energy conservation in small- and medium-sized enterprises (SMEs). In this paper, we propose a new method consisting of operating time estimation and simplified disaggregation based only on the SME's hourly electricity demand data and actual outdoor temperature data. The method first estimates the operating time using a self-organizing map, which categorizes the hourly data as operating, half-operating, or non-operating time and eliminates manual parameter adjustment that requires user trial and error. Then, using the estimated operating time, our simplified disaggregation method estimates hourly air conditioning (AC) demand considering the variation in each office's temperature at which air conditioners are switched on. The method was evaluated using a public business energy management system dataset from Japan, and the average errors of yearly AC demand estimation were better than 10%. The outputs of the proposed method may provide information that is more relevant, reliable, and specific to each customer and efficiently promote energy conservation measures in SMEs. [ABSTRACT FROM AUTHOR]

Existing studies pay little attention to when or under which conditions foreign direct investment (FDI) can spill energy-saving technologies. From a perspective of energy-biased technical change and using a two-layer nested constant elasticity of substitution (CES) production function, this paper investigates the energy-saving spillover effect of FDI on technical change (i.e., energy-saving spillovers) in China between 2002 and 2015. In particular, we consider the conditions of marketization, industry, technology, and labor mobility to examine whether and when FDI has energy-saving spillovers. The results indicate no income inequality effect, i.e., there is no evidence supporting that FDI flowing into low- and middle-income regions increases energy consumption, while FDI flowing into high-income regions conserves energy. However, there is a condition effect: FDI can improve (support the halo effect) or deteriorate (contradict the halo effect) the environmental performance under different conditions. Moreover, there is a threshold effect: the direction of FDI spillovers varies with the different levels of the threshold variables. An increasing marketization motivates enterprises to select energy-biased technologies. It is more likely to generate energy-saving spillovers in the regions with a lower specialized agglomeration level. FDI will have energy-biased spillovers when domestic technological level is relatively high with an evident energy-biased technology. In addition, a moderate labor mobility is beneficial to the energy-saving spillovers of FDI. [ABSTRACT FROM AUTHOR]

Hydraulic press machines (HPMs) are often preferred in metal processing for their high power-to-mass ratio, stiffness, and load capacity. Unfortunately, they are also known for their high energy consumption and low energy efficiency. The mismatch between installed and demanded power is the primary cause of low energy efficiency among HPMs. This paper proposes an energy-saving hydraulic drive system based on the flywheel energy storage system (FESS) to reduce the installed power and improve the energy efficiency. The FESS is used to store redundant energy when the demanded power is less than the installed power. During pressing with slow falling, the stored energy is recycled in combination with an AC motor to work against the heavy load. Furthermore, unlike traditional FESS's, the variable frequency drive (VFD) and specific control algorithm are adopted to improve the energy storage capacity of the FESS, as well as the operation and lifetime of the hydraulic drive system. The proposed method presents the first integration of both VFD and FESS into a hydraulic drive system. Using this method, the low energy efficiency caused by high installed power can be significantly improved, and the negative effects caused by mismatches between the installed and demanded power can be largely mitigated. Finally, a test platform is set up to verify the effectiveness of the proposed hydraulic drive system. The results show that the installed power and energy consumption are reduced by approximately 30% and 40%, respectively, based on the working characteristics of a traditional drive system. • An energy-saving hydraulic drive system based on the flywheel energy storage system and variable frequency control is developed. • The installed power and average energy consumption of the traditional hydraulic press machines can be reduced by 30% and 40%, respectively. • The proposed hydraulic drive system has the same working characteristics and processing properties as the traditional drive system. [ABSTRACT FROM AUTHOR]

Research on energy-saving retrofits of the existing industrial systems can effectively improve the systems' energy-saving level and increase company revenue. The energy-saving bottleneck diagnosis for systems is the foundation of the energy-saving retrofit design. The diagnosis also aims to yield a more targeted energy-saving retrofit, thus ensuring the energy-saving effect and investment income of the transformation project. Therefore, this paper proposes a new method to diagnose the energy-saving bottleneck of the existing industrial systems. First, the energy-saving bottleneck diagnosis is decomposed into two parts: the diagnosis of energy-saving bottleneck units and the diagnosis of energy-saving bottleneck factors. Then, the energy density decomposition method is improved and a multi-mode energy efficiency density decomposition method is proposed. Next, an energy-saving bottleneck diagnosis method for industrial systems based on multi-mode energy efficiency density decomposition and an orthogonal test method is proposed. The proposed method can analyze the energy-saving potential of existing systems, accurately locate energy-saving bottlenecks, and avoid the dependence on a large amount of expert experience or complete production data in the existing methods. Finally, the proposed method is applied to an actual circulating cooling water system and the system energy-saving bottleneck diagnosis is realized, thus verifying the practicability of the method. • Energy-saving bottlenecks in industrial systems are analyzed and decomposed. • A method for energy-saving bottleneck diagnosis from two directions is proposed. • An improved energy efficiency density decomposition method is proposed. • The diagnosis of energy-saving bottlenecks in industrial systems is achieved. • The verification is performed for an industrial circulating cooling water system. [ABSTRACT FROM AUTHOR]

For many years, China has invested effort into achieving resource conservation and environmentally friendly economic development. The measures implemented include promoting technological innovation, market-oriented reform and industrial structure adjustment. But there are open questions: What have the effects of these measures been? Have they helped China achieve win-win development in terms of its resources, environment, and economy? This paper aims to answer these questions by using a newly proposed global three-hierarchy meta-frontier Luenberger index that can quantify the effects of promoting technological innovation, industrial restructuring, etc., on economic growth, energy conservation and emissions reduction. The results show that (1) during 2001–2016, China's energy efficiency, economic efficiency, and emissions efficiency improved, indicating that China's efforts have brought about the win-win development of resources, the environment, and the economy; (2) the implemented measures have heterogeneous effects on economic growth, energy conservation, and emissions reduction. The implemented measures are generally beneficial to energy conservation and emissions reduction but potentially harmful to the economy; (3) the effects of these measures on economic growth, energy conservation and emissions reduction across China's regions and provinces are also quite different, indicating that local provincial governments should have a clear understanding of their own characteristics before implementing the relevant policies and measures. • A global three-hierarchy meta-frontier Luenberger index was proposed for the study. • It can quantify the effects of China's efforts on economy, energy, and emissions. • Results show that China's efforts have brought about a win-win development. • There are the heterogeneities of China's pathways to economic growth, energy conservations and emissions reductions. • The future strategies for China's win-win development should take the heterogeneities into account. [ABSTRACT FROM AUTHOR]

[Display omitted] • Calculate carbon emissions of energy consumption by using annual emission factors of indirect energy. • Put forward quantitative indicators of mine intelligence. • Impact of intelligence on carbon emissions of energy consumption in mining industry. • Prediction of carbon emissions in mining industry based on intelligent scenario analysis. The intelligent construction of the mining industry has become an emerging trend in its future development plans. As a traditional high energy consumption industry, its energy conservation and emissions reduction have become particularly urgent concerns. Therefore, it is of great significance to study the impact of intelligence on the carbon emissions from energy consumption in the mining industry. This paper first uses dynamic indirect energy emissions factors to build an accounting model for carbon emissions from energy consumption in the mining industry and calculates the related carbon emissions from 2001 to 2020 by sector. Then, an expandable stochastic environmental impact assessment (STIRPAT) model is established to analyze the impact of intellectualization on the carbon emissions intensity of China's mining industry. At the same time, many other important factors are included in the model, and the model is fitted using ridge regression. The results show that the order of influence, from large to small, is as follows: energy intensity, industrial output value, per capita output value, the proportion of output value of large and medium-sized enterprises, per capita stock of fixed assets, average enterprise output value, R&D intensity and proportion of indirect energy. Each 1% increase in the above factors will increase the corresponding carbon emissions intensity by 0.2005%, −0.1271%, −0.1207%, 0.7467%, −0.0595%, −0.09813%, 0.1143% and −0.1419%, respectively. Then, the prediction values of the independent variables are obtained by using the scenario analysis method and grey prediction model, and 12 scenarios are set up to predict the carbon emissions intensity of the mining industry energy consumption from 2021 to 2035 by using the expanded STIRPAT model. By analyzing the prediction results, we conclude that Scenario 8, Scenario 9, Scenario 10, Scenario 11 and Scenario 12 can achieve the emissions reduction goals of the mining industry in 2025 and 2030; the optimal path of emissions reduction is given for various scenarios. Finally, a development strategy for emissions reduction in China's mining industry is proposed. [ABSTRACT FROM AUTHOR]

The annual consumption of petroleum products in India was about 221 million metric tons in 2015. Of this, 84% was imported. The Indian industrial sector accounts for about 16%-20% of the total fuel consumption for thermal energy for different heating applications in the temperature range of 50°C--250 °C. Solar collectors can meet these temperature requirements and offer the possibility to mitigate the consumption of oil. This study highlights the fact that conversion efficiency from solar energy is much higher for process heating than for electricity generation and that process heating applications constitute a significant share of industrial energy consumption. In this paper, a methodology has been developed to estimate the potential for integration of solar collectors for process heating. The methodology employs process operating temperatures to select the type of solar collectors. The size of the solar field is estimated taking into account the thermal heat loads, working fluid and temperatures of these processes, the efficiency of the chosen solar collectors, location-based solar irradiance and capacity utilization of the solar collectors. The proposed methodology has been validated with a software tool called System Advisor Model (SAM). The techno-economic analysis will indicate the viability of solar systems for integration in industries. Therefore, the consociated parameters on economic (capital cost, fuel oil savings, monetary benefits), financial (Payback periods, Rates of Returns) and environmental (Carbon savings) are estimated. Further, the methodology has been applied to select Indian industries to verify its potential quantitatively. The industries selected include Textile, Pulp & Paper, Dairy, Leather and Automobile. Process-wise energy demands are considered while estimating the potential as the fuel requirement offset by solar energy in terms of absolute fuel oil savings, monetary benefits and carbon savings. The other economic and financial parameters mentioned above were estimated to verify the capability and present the market position of solar systems. Further, sensitivity analyses have been performed with respect to solar energy penetration and fuel oil prices to address the viability of integration of solar energy for process heating. [ABSTRACT FROM AUTHOR]

This paper investigates CO 2 emissions produced by two different container terminal operating models (tire transtainers and rail transtainers) at the port of Kaohsiung, and seeks to determine energy saving and CO 2 reduction strategies for shipping companies and terminal operators in order to comply with green port requirements. This paper's research methodology involves carbon footprint analysis and gray relational analysis. Carbon footprint analysis is employed to calculate the CO 2 emissions per container of two different container terminal operating models employing data for various areas (such as the berthing area, container yard, and gate area), where the raw data is collected from shipping companies or terminal operating companies at the port of Kaohsiung. Gray relational analysis is then used to determine the ranking order of different container terminal operating models based on the green port assessment criteria of working time efficiency, energy consumption, and CO 2 emissions. The chief findings of this paper are that the ranking order of container terminal types based on carbon footprint is tire transtainer ahead of rail transtainer; optimal green port assessment criteria constituted working time efficiency, energy cost, and CO 2 emissions; and use of container handling equipment with high operating efficiency can not only accomplish work rapidly, lessening the berthing time of ships in port, but also reduce energy costs and CO 2 emissions. [ABSTRACT FROM AUTHOR]

A review of 14 leading energy efficiency program administrators demonstrates that high levels of energy savings are not only achievable but also sustainable. Despite substantial challenges that may limit savings opportunities in traditional areas, energy savings have increased from year to year. Average net energy savings levels have doubled for the administrators in this sample, from 0.9% in 2008 to 1.8% in 2014. Utility-sector energy efficiency programs have grown substantially in the past decade. According to the most recent ACEEE State Energy Efficiency Scorecard, electric-efficiency program investments have increased from approximately $1.5 billion in 2006 to over $5.9 billion in 2014. As savings levels have increased, national leaders in program administration have emerged. In this paper we review annual program performance for 14 leading energy efficiency program administrators. We sought diversity in the selection of program administrators and included administrators of varying service-territory size and geographic location. Among the 14 program administrators we reviewed, none achieved electricity savings of 1.5% of retail electric sales in 2009. By 2014, 8 of the 14 were achieving savings higher than 1.5%, with 4 of the 8 saving more than 2%. The data also show that high savings are sustainable over time. We find that as savings rise, levelized costs of energy efficiency remain relatively flat. This paper also seeks to identify the drivers of high savings. State or local policy requirements to meet specific energy-efficient savings goals guide all program administrators in this study. The design of the targets and goals vary, but these policies are critical in driving high levels of savings. [ABSTRACT FROM AUTHOR]

It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout programmes. [ABSTRACT FROM AUTHOR]

Highlights • Ruled-based algorithm to detect anomalies in compressor based appliances. • Evaluation of the viability of NILM for anomaly detection. • Experiments using publicly available four NILM algorithms and three datasets. • Analysis of correlation between NILM accuracy and resulting anomaly detection. • Discussion of further steps to facilitate effective anomaly detection using NILM. Abstract Identification of faulty appliance behaviour in real time can signal energy wastage and the need for appliance servicing or replacement leading to energy savings. The problem of appliance fault or anomaly detection has been tackled vastly in relation to submetering, which is not scalable since it requires separate meters for each appliance. At the same time, for applications such as energy feedback, Non-intrusive load monitoring (NILM) has been recognised as a scalable and practical alternative to submetering. However, the usability of NILM for anomaly detection has not yet been investigated. Since the goal of NILM is to provide energy consumption estimate, it is unclear if the signal fidelity of appliance signatures generated by state-of-the-art NILM is sufficient to enable accurate appliance fault detection. In this paper, we attempt to determine whether appliance signatures detected by NILM can be used directly for anomaly detection. This is carried out by proposing an anomaly detection algorithm which performs well for submetering data and evaluate its ability to identify the same faulty behaviour of appliances but with NILM-generated appliance power traces. Our results on a dataset of six residential homes using four state-of-the-art NILM algorithms show that, on average, NILM traces are not as robust to identification of faulty behaviour as compared to using submetered data. We discuss in detail observations pertaining to the reconstructed appliance signatures following NILM and their fidelity with respect to noise-free submetered data. [ABSTRACT FROM AUTHOR]

Abstract This paper is a multi-case study of three oil and gas projects in Brazil that investigates the tacit knowledge on how to best manage energy use during the construction phase. Twenty-two in-depth interviews form the primary evidence of the case studies. The methodology incorporates network analysis of concepts that influence energy use to formalize the case studies and collect additional knowledge. The framework comprises thirteen construction activities and twenty-one systems and factors related to design, resources and site characteristics, specific to the construction domain. This set of concepts can influence the energy consumption during the construction phase. Although these concepts are present in nearly every project, their dynamics and their inter-relations with project-specific systems change, depending on the context of the project. The proposed method, which employs network analysis, help identify their interrelationships and relative importance of these concepts. In addition to the opportunities and lessons learned that can guide future energy assessments and improve energy use in construction projects, the results show how promising network analytics is to obtain knowledge usually difficult to perceive in traditional knowledge processes. Besides, this work highlights the importance of supporting collaboration between project team members to study and decide on the plans to manage/reduce energy consumption during the construction phase in oil and gas facilities. While the energy savings are smaller than the upstream and operation phases, it has received much less attention. It is also a phase with much more subjective and contextualized knowledge – making the use of case-based analyses more suitable. Highlights • Supports collaboration in studying and planning energy use during the construction phase of O&G projects. • Three case studies are conducted supported by in-depth interviews. • Network analysis is used to represent construction activities, factors and systems. • The approach represents a meaningful way to formalize the knowledge in the cases. • Can help formalize the knowledge contained in a text case. [ABSTRACT FROM AUTHOR]

Highlights • A electro-hydraulic energy-saving system is proposed for energy recovery and regeneration. • A parametric rule-based strategy of the proposed system is developed for real-time control. • The proposed energy-saving system prototype is equipped on a 23-ton hydraulic excavator. • Experiments studies show that about 17.6% energy can be saving by this system. • The system is evaluated from multiple perspectives of efficiency, performance and comfort. Abstract Energy recovery and regeneration comprise an effective way to improve hydraulic excavator fuel economy. This paper proposes a novel electro-hydraulic energy-saving system to integrate recovery and regeneration devices. The working conditions of excavators and system configuration of the proposed system are analyzed. A parametric rule-based strategy is implemented to real-time control. A energy-saving prototype is developed for a 23-ton hydraulic excavator. Several tests are carried out to evaluate the prototype from the perspectives of energy consumption, action performance, and system comfortableness. Results show that the excavator equipped with the energy-saving system can be reduced by approximately 17.6% compared with a conventional one. The braking time of the boom-lowering process is about 1.87 times that of the conventional system. The electric motor is the main vibration source, and the impact of hydraulic system shock on comfortableness is indirect. The results are important reference for further system optimization. [ABSTRACT FROM AUTHOR]

Abstract Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation. This paper describes the development and testing of a hybrid data-driven and physics model-based operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system. [ABSTRACT FROM AUTHOR]

Highlights • Providing a critical review of office building energy management based on occupancy monitoring and modeling, and HVAC and lighting control strategies. • Proposing a roadmap towards cognitive building management. Abstract Buildings are responsible for a large portion of global energy consumption. Therefore, a detailed investigation towards a more effective energy performance of buildings is needed. Building energy performance is mature in terms of parameters related to the buildings' physical characteristics, and their attributes are easily collectable. However, the poor ability of emulating reality pertinent to time-dependent parameters, such as occupancy parameters, may result in large discrepancies between estimated and actual energy consumption. Although efforts are being made to minimize energy waste in buildings by applying different control strategies based on occupancy information, new practices should be examined to achieve fully smart buildings by providing more realistic occupancy models to reflect their energy usage. This paper provides a comprehensive review of the methods for collection and application of occupancy-related parameters affecting total building energy consumption. Different occupancy-based control strategies are investigated with emphasis on heating, ventilation, and air conditioning (HVAC) and lighting systems. The advantages and limitations of existing methods are outlined to identify the gaps for future research. [ABSTRACT FROM AUTHOR]

Abstract Energy conservation and emissions reduction have been the focus of the road transport sector due to its energy-intensive and air pollution-intensive nature. It also embodies the imprints of human technological progress. This paper compares the energy consumption of the road transport sector in China and OECD countries. We used time series and panel data, co-integration analysis, error correction model and dynamic model to study the impacts of the major influencing factors such as income, income structure, urbanization, price, and technological advancement on gasoline and diesel consumption in road transport. Overall, China's fuel consumption is influenced mainly by investment, government expenditure and demographic changes such as urbanization, while the fuel consumption of OECD countries is mainly driven by household final consumption expenditure. In the short run and from a dynamic perspective, China's fuel consumption is less influenced by its historical consumption than current influencing factors, while that of OECD countries is more affected by historical fuel consumption. Based on the analysis and findings of this study, some policy implications are proposed. [ABSTRACT FROM AUTHOR]

Abstract Large amount of energy is consumed to meet user satisfaction in public buildings, which causes carbon emission and results in environmental problem. A direct method to reduce this carbon emission is restricting opening area in public buildings. However, restricting opening area causes occupant satisfaction reduction. Trade-off carbon emission and occupant satisfaction needs to be concerned in building operation strategy design. In this paper, we developed a new integrated modelling method to trade-off between the carbon emission and occupant satisfaction of restriction strategies. We designed a case study in the sixth teaching building (STB) in Tsinghua University. Survey and field investigation were conducted to obtain data and build model. Integrated model result showed that satisfaction increases with the increase of carbon emission, while there exists cluster division and marginal diminishing patterns. By analyzing the performance of strategies with different calculation methods, we found that most optimal strategies open section B and C in STB. Meanwhile, when occupant number is 1,000, 2000 and 4,000, corresponding average satisfaction is 71.4%, 58.8% and 40.4%. It indicates that optimal operation strategy performance changes with the increase of occupant number, but the fluctuation is relatively mild. This integrated model has prospect in guiding energy conservation operation strategy design. It also supports real time monitoring and has potential in interactive platform application. Highlights • We developed a tradeoff model with carbon emission and occupant satisfaction in public building. • Our model found out optimal restriction strategies and strategy performance patterns. • Strategy performance slightly changes with the increase of occupant number. [ABSTRACT FROM AUTHOR]

Abstract The construction industry is one of the largest energy consumers and CO 2 emitters in China. This paper primarily aimed to explore the driving forces of energy-related CO 2 emissions (ECE) from the construction industry. Adopting a comprehensive decomposition approach, this study decomposes the changes in ECE into eight factors. The main results provide the following findings. (1) Industrial activity was the largest factor pushing the growth of CO 2 emissions, driving up CO 2 emissions in all years and contributing to a 174.65 Mt CO 2 emissions increase in total. (2) In contrast, advances in industrial output technology represented the dominant factor inhibiting CO 2 emissions, cumulatively reducing CO 2 emissions by 99 Mt. (3) The effects of potential energy intensity changes and industrial output technical efficiency fluctuated in different years but exerted positive effects on CO 2 emissions over the entire period. (4) Additionally, changes in spatial structure, energy-saving technology, and energy consumption structure as well as energy usage efficiency all contributed to emissions reductions to varying degrees. The CO 2 emissions of the construction industry increased in all provinces, while emissions changes and their factor effects varied distinctly across provinces. Highlights • Direct and indirect CO 2 emissions in China's construction industry are calculated. • Impacts of driving factors are quantified by combining the PDA and IDA methods. • Industrial activity is mainly responsible for CO 2 emissions increase. • Industrial output technology change is the dominant curbing factor. • Factor effects of China's construction industry vary distinctly across provinces. [ABSTRACT FROM AUTHOR]

Highlights • Parameters of 60 electricity-saving measures and 16 abatement measures are compiled. • Synergies of electricity use in China's chemical sector at grid level are assessed. • Efficient use of power can cut 154 Mt CO 2eq , 295 kt SO 2 , 322 kt NO x and 64 kt PM 2.5. • North and Northwest grids have the largest economic benefits on emissions reduction. • Grid with high electricity price is more sensitive to price change than other grids. Abstract Industry is a large electricity user. China's chemical industry (globally the largest based on sales) contributes 7% to China's GDP, while it consumes 11% of the total electricity consumption in industry and is responsible for 40% of total CO 2eq , 40% of SO 2 , 59% of NO x and 18% of PM-emissions of the chemical industry emissions. The heterogeneity of GHG and air pollutant emissions across electricity grids (within a country) is rarely included in analyses. In this paper, electricity conservation supply curves are developed (distinguishing the grids) to estimate the cost-effective and technical potentials of electricity conservation in China's chemical industry. The emission factors per grid for GHG (i.e. CO 2 , CH 4 and N 2 O) and air pollutants (i.e. SO 2 , NO x and PM 2.5) are calculated and used to quantify the emissions mitigation achieved by electricity saving technologies in the chemical industry for the period 2012–2035. Results show that significant multiple benefits can be obtained by implementing electricity efficiency measures. There are large differences among the six grids in terms electricity savings and emissions abatement of GHG and air pollutants. 83% of the total electricity saving potential is contributed by the North, Northwest and Central grids, equal to 32% of baseline electricity consumption in 2035. In 2035, 129 Mt of CO 2 , 33 kt CO 2eq of CH 4 , 571 kt CO 2eq of N 2 O, 235 kt of SO 2 , 275 kt of NO x and 52 kt of PM 2.5 in these three grids can be avoided as a result of electricity savings (a reduction of 31–33% compared to baseline emissions). When decision-makers set targets for energy saving and emission reduction, the multiple benefits and grid heterogeneity should not be ignored. [ABSTRACT FROM AUTHOR]

Highlights • Taiwanese building energy conservation index (ENVLOAD) was converted to OTTV. • New models for energy efficient envelope were proposed as an adaption to the changing climate. • Coefficients of the OTTV model should be changed to reflect climate change. • Thermal insulation is gaining more concern while climate change proceeds. • Altering method of OTTV and setting new criteria in response to future climate was proposed. Abstract Changes in the climate system affect a building's energy consumption. Many countries have implemented building energy consumption indices to regulate the newly built buildings from excessive energy use. However, those indices were developed based on previous climate datasets and were incapable of responding to the changing climate. To stabilize the energy uses of future buildings as those under today's climate, regular updates of the evaluation index are mandatory to take climatic variations into consideration. This study firstly discussed the limitations in responding to future climate of current practicing building envelope energy conservation index in Taiwan. Secondly, we proposed a new index, the overall thermal transfer value (OTTV), based on the simulation results and tried to convert existing baseline criteria for the new OTTV. An approach on how to alter the OTTV index in response to the climate change was proposed. We found that because of the warming climate, the coefficient of the OTTV equation has to be changed over times to accurately respond to the changing climate. The approach presented in this paper provides a method on how to formulate a climate responsive index in pursuit of a better energy efficient building in the future. [ABSTRACT FROM AUTHOR]

Highlights • Non-intrusive techniques are proposed to infer occupancy from existing HVAC sensors. • Reheat energy use is reduced by more than 38% with better nighttime setback schedules. • Derived a comprehensive set of candidate anomalous zones using unsupervised learning. • Proposed approach can be readily applied to any commercial building with a BMS. Abstract The Heating Ventilation and Air Conditioning (HVAC) system of commercial buildings traditionally runs on a fixed schedule that does not take occupancy into account despite its huge variation over space and time, thereby wasting a lot of energy in conditioning empty or partially-occupied spaces. Occupancy information is essential to eliminate wasteful energy use with imperceptible impact on building operations and human comfort. This paper investigates the application of non-intrusive techniques to obtain a rough estimate of occupancy from coarse-grained measurements of sensors that are commonly available through the building management system. Various static and adaptive energy-efficient schedules are developed based on this approximate knowledge of occupancy at the level of individual zones. Our experiments in three large commercial buildings confirm that the proposed techniques can uncover the recurring occupancy pattern of the zones, and schedules that incorporate these occupancy patterns can achieve more than 38% reduction in reheat energy consumption while maintaining indoor thermal comfort. [ABSTRACT FROM AUTHOR]

Housing Stock Energy Models (HSEMs) play a determinant role in the study of strategies to decarbonise the UK housing stock. Over the past three decades, a range of national HSEMs have been developed and deployed to estimate the energy demand of the 27 million dwellings that comprise the UK housing stock. However, despite ongoing improvements in the fidelity of both modelling strategies and calibration data, their longevity, usability and reliability have been compromised by a lack of modularity and openness in the underlying algorithms and calibration data sets. To address these shortfalls, a new open and modular platform for the dynamic simulation of national (in the first instance, the UK) housing stocks has been developed—the Energy Hub (EnHub) . This paper describes EnHub’s architecture, its underlying rationale, the datasets it employs, its current scope, examples of its application, and plans for its further development. In this we pay particular attention to the systematic identification of housing archetypes and their corresponding attributes to represent the stock. The scenarios we analyse in our initial applications of EnHub, based on these archetypes, focus on improvements to housing fabric, the efficiency of lights and appliances and of the related behavioural practices of their users. In this we consider a perfect uptake scenario and a conditional (partial) uptake scenario. Results from the disaggregation of energy use throughout the stock for the baseline case and for our scenarios indicate that improvements to solid wall and loft thermal performance are particularly effective, as are reductions in infiltration. Improvements in lights and appliances and reductions in the intensity of their use are largely counteracted by increases in heating demand. Housing archetypes that offer the greatest potential savings are apartments and detached dwellings, owing to their relatively high surface area to volume ratio; in particular for pre-1919 and inter-war epochs. [ABSTRACT FROM AUTHOR]

This paper aims to present a novel smart grid adaptive energy conservation and optimization engine for smart distribution networks. The optimization engine presented in this paper tries to minimize distribution network loss, improve voltage profile of the system and minimize the operating cost of reactive power injection by switchable shunt Capacitor Banks using Advanced Metering Infrastructure data. Moreover, it performs Conservation Voltage Reduction (CVR) and minimizes transformer loss. To accurately weight the optimization engine objective function sub-parts, Fuzzification technique is employed in this paper. Particle Swarm Optimization (PSO) is applied as Volt-VAR Optimization (VVO) algorithm. Substantial benefits of the proposed energy conservation and optimization engine include but not limited to: adequate accuracy and speed, comprehensive objective function, capability of using AMI data as inputs, and ability to determine weighting factors according to the cost of each objective sub-part. To precisely test the applicability of proposed engine, 33-node distribution feeder is used as case study. The result analysis shows that the proposed approach could lead distribution grids to achieve higher levels of optimization and efficiency compared with conventional techniques. [ABSTRACT FROM AUTHOR]

A fundamental problem in the management of energy usage is the inability to clearly predict any possible energy saving opportunities. The cost of both under or overestimating potential returns on investment can be prohibitive to a decision maker. In recent years the simulation of energy usage using existing manufacturing simulation tools has increased in popularity among researchers, but it is decision makers who need to see the benefits of this discipline. This paper proposes an interactive manufacturing energy management toolkit which makes use of existing productivity simulation models for the prediction of energy usage. An interactive Microsoft ® Excel ® based toolkit is developed to control Lanner's WITNESS ® discrete-event simulation software using Microsoft ® Visual Basic ® for Applications. The toolkit has the ability to predict potential areas where energy saving opportunities can be made within a complex manufacturing line, and is accessible from management presentations and proposals. The interactivity of the toolkit provides an environment which facilitates efficient hypothesis testing. The paper includes an industrial case study where the approach was used to quantify theoretical savings from certain energy usage reduction scenarios within a complex automotive engine manufacturing line. [ABSTRACT FROM AUTHOR]

Coal mining not only produces, but also consumes a large amount of energy. Coal production has an extremely high energy efficiency potential, and benchmarking is critical to discover this potential. To address problems such as ambiguity of coal production (e.g. underground mining), benchmarking range and absence of both indicators and standards for energy efficiency benchmarking, this paper makes use of product-based and process-based benchmarking. These two techniques are used to construct a benchmarking system for coal production with high energy efficiency, and improve the standards used at the eight coal mines owned by the Yankuang Group. Comparison and analysis of benchmarking in enterprises indicates that energy use during raw coal production could be reduced by 22.77%. Across China energy savings from coal production could reach 15.98–32.98 Mtce, with an equivalent 42–87 Mt of CO 2 emissions saved. Lastly, this paper provides measures to improve coal production energy efficiency. [ABSTRACT FROM AUTHOR]

Building energy consumption accounts for a large portion of total energy-use in a city or a regional district. However, energy load spatial distribution has seldom been considered during urban design phase. And energy conservation and energy efficiency measures pay more attention to individual building than buildings in a district or regional space as a whole. If buildings with different functions are mixed together and share same energy system, the savings on system capacity and peak electricity load can be significant. In this paper, a load superposition concept is proposed. The term ‘superposition’ refers to overlapping of energy demand load curves from different buildings and so that the total peak is smaller than the sum of individual peaks. Three spatial optimization methods of demand side load management and three different schemes of energy systems are proposed in this paper. And economic analysis is recommended to evaluate the different energy systems. The applicability of different approaches and the significance of load superposition was analyzed and elaborated through a case study to offer planners a feasible way for evaluating the potential of load spatial optimization. [ABSTRACT FROM AUTHOR]

Significant increases in industry energy efficiency are crucial for the transformation of the world's energy systems. Many production sites offer high energy savings capabilities, and if these are accompanied by short periods of economic amortization, companies should be willing to act. This case study provides a novel and extended energy assessment for plastics processing plants including primary energy, greenhouse gas emissions, and energy costs. The research distinguishes between the standard form of separate individual energy assessments and provides a more innovative holistic approach taking all relevant energy flows within the production system into account. Dynamic simulation offers a quick and effective way to predict the results of the possible energy saving measures highlighted in this analysis. The paper presents validated energy consumption simulations based on realistic processing conditions for two injection molding factories in different climatic zones. The results show that combining a number of separate energy saving measures can reduce the primary energy demand by around 26% for a German plant under temperate climate conditions and 20% for a Western Australian plant under Mediterranean conditions. However, when the separate energy saving measures are holistically combined the reduction in energy use significantly increases to 41% and 43% respectively. This holistic energy strategy involves incorporating better cogeneration and waste heat recovery options. For small and medium sized companies in particular major energy infrastructure investments may often be considered too expensive without examining the extended benefits from a holistic energy assessment perspective. In contrast, a holistic framework, like the one suggested in this paper could provide a number of new options for increasing energy efficiency that individually might normally not be accepted under conventional economic rate of return analysis. [ABSTRACT FROM AUTHOR]

Global trends in reapplication of wood re-application and wood-based products, used as construction and architectural coating materials, are present not only because of esthetic, artistic and design requirements or tradition and nature-based inspiration, but also because wood adapts to modern trends in sustainable development. This paper presents traditional influence on application of wood as a façade cladding element in modern architecture of Serbia and provides the analyses of functional and design potentials of wood as an element of ventilated façade systems and its influence on energy efficiency. The objective of this research was to provide a more detailed insight into potentials and limitations of the application of wood and wood-based products, as façade cladding elements used for construction and reconstruction of architectural objects, in the climate conditions of Belgrade (Serbia) as well as to point out certain suggestions about improving their energy efficiency characteristics. The paper presents the examples of various methods of applying wood and wood-based products as elements of façade cladding in modern architecture of Belgrade. The main focus is put on an object that underwent different refurbishment methods and façade system alterations in order to meet the requirements of energy saving. [ABSTRACT FROM AUTHOR]