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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]

The construction sector is responsible for around 39% of global greenhouse gas emissions, with building envelopes playing a significant role in this carbon footprint. This is particularly relevant for building facades with large window-to-wall ratios (WWR), because higher proportions of glass increase both operational and embodied carbon. In light of the contemporary demand for such glazed envelopes, there is an emerging legislative trend that measures not only operational carbon, but also embodied via utilizing life-cycle assessment tools (LCA). To this effect, there is a great demand for techniques that can effectively lower the former carbon without increasing the latter. To contribute to this effort, this paper presents a whole LCA across seven cities for standard and advanced glazing solutions, including Water-Filled Glass (WFG). WFG is a novel technology that utilizes water in the cavity of insulated glass units (IGU), to improve thermal comfort, energy and acoustic performance of buildings. The paper utilizes energy simulation modelling through TRNSYS, and provides a comparative analysis of different glazing techniques. The significance of this paper is that a comprehensive cradle-to-grave LCA analysis of standard and advanced glass techniques is presented here for the first time, across all major Köppen-Geiger climates. The results show that WFG presents considerable carbon savings against all other options e.g., 31–53% compared to double glass, underlining the possibility of reducing operational and embodied carbon simultaneously. A second novel outcome of the paper is introducing the Glass Viability Index (GVI) for the first time, which presents the 'carbon payback' period required to offset an embodied carbon increase with operational savings. This is because the short lifespan of glass makes embodied carbon a large driver in LCA, and as such the prioritisation of operational carbon when selecting a technique can lead to a unsustainable practice. • Glass options should be ranked on LCA instead of just operational energy. • Paper presents life-cycle carbon for seven glass options across all major climates. • Viability of advanced glass options is affected by their high embodied carbon. • Glass option ranking depends on length of representative study period (RSP). • Glass Viability Index is created, which ranks carbon impact independently of RSP. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has important significance for hybrid electric UAVs. This paper aims at improving energy efficiency by maximizing utilization solar energy, reducing demand power and optimizing power allocation. The flight trajectory optimization and energy management are considered synthetically, and the coupled model is established. Adaptive sequential convex programming method (ASCP) is further proposed to solve this coupled model. The adaptive discretization method, fuzzy-based trust region update mechanism and two-stages solving strategy are proposed to improve its optimality and convergence. The optimized flight trajectory and global optimal battery S O C trajectory can be applied to trajectory tracking control and online energy management. Numerical simulation results show that ASCP can improve energy efficiency of hybrid electric UAVs. Compared with uniform discretized sequential convex programming (UDSCP) and Gauss pseudo-spectral method (GPM), it can improve solar energy by 9.3% and 24.1%. Energy management experiment results indicate that ASCP has excellent energy saving effect. Compared with nonlinear model predictive control (NMPC), fuzzy logic state machine (FLSM) and passive energy management strategy (PEMS), it can save hydrogen by 13.5%, 18.3%, and 22.2%, respectively. This work is conducive to promoting the application of renewable energy in UAVs. • Energy efficiency is improved by flight trajectory and energy management coupled optimization. • The flight trajectory optimization and energy management coupled model is established. • Adaptive sequential convex programming method is proposed to solve the coupled model. • Fuzzy logic control is proposed to adaptively adjust the sizes of trust region. [ABSTRACT FROM AUTHOR]

Industrial parks allow industries to share infrastructure and thus saving money, finally redounding in improving the economy of many countries worldwide. Given the objectives of carbon neutrality imposed by different entities, it results mandatory promoting energy efficiency in industrial parks. Aligning with such objective, encouraging industries to provide energy flexibility becomes essential. In the electricity sector, such flexibility can be provided through optimally managing local assets such as energy storage and flexible loads. However, flexibility provision should be promoted by implanting proper pricing mechanisms. This paper focuses on this issue by developing a local market clearing mechanism for industrial parks, whose main novelty redounds in the inclusion of a fair pricing mechanism through which industries are paid by flexibility provision. Different types of flexible loads are considered and modelled (i.e. curtailable, interruptible and deferrable), so that the new proposal is suitable for leveraging fully capabilities of industrial flexible loads. The whole pricing mechanism is raised as a bi-level game-based model, by which local energy and flexibility prices are revealed in a coordinated way. Challenges brought by the inclusion of binary variables (needed for modelling some types of flexible loads) are solved by proposing a solution algorithm based on the well-known Column & Constraint Generation Algorithm. The resulting optimization framework is Mixed Integer Linear Programming, being therefore solvable by off-the-shelf solvers. A case study is presented to validate the new proposal as well as highlight some important aspects related to local markets in industrial parks and its practical implantation. • A new market model for electricity provision in industrial parks is proposed. • Different flexible loads are modelled including binary variables. • Binary variables are accommodated in a developed Benders-based algorithm. • Local storage has a direct impact on shaving peak load prices. • Renewable generation may discourage to partake in flexibility provision. [ABSTRACT FROM AUTHOR]

Investing in energy efficiency measures is a major challenge for SMEs, both for environmental and economic reasons. However, certain barriers often make it difficult to invest in such measures. Although public financial support helps to overcome economic barriers, public bodies face the challenge of identifying which SMEs display the greatest potential to invest in energy efficiency measures. By applying a random forest technique and by using sampling balancing techniques, this paper identifies the profile of industrial SMEs that might be potential beneficiaries of public aid, thereby helping public institutions to target their calls and direct their efforts towards this group of SMEs. Specifically, liquidity and indebtedness are found to be the most useful predictors for SMEs in the industrial sector. The results are robust and reveal that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than applying traditional estimation techniques. By identifying potentially benefiting firms, this work helps to boost the effectiveness of public subsidies and to improve the channeling of public funds, which ultimately favors investment in energy efficiency. • Public subsidies favor energy efficiency measures by reducing the up-front cost and making the investment more profitable. • Subsidy effectiveness depends on the capacity to identify which SMEs are potential beneficiaries of energy subsidies. • We evidence that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than traditional techniques. • The most useful predictors for SMEs in the industrial sector are related to liquidity and indebtedness. [ABSTRACT FROM AUTHOR]

Abstract: The energy use at a Swedish chemical wood-pulp mill is compared internationally and for two model mills that aim to use the most efficient available technology. The international comparison is performed between Canadian and Scandinavian pulp-mills on a general level, and on a closer level among eleven Swedish and Finnish non-integrated sulfate pulp-mills, the type of mill considered in the case study. The two model mills that are used for comparison are one Swedish and one Canadian. The Scandinavian pulp-mills are somewhat more energy efficient than the Canadian mills. Still, the variation in energy use is remarkably large among the Scandinavian mills, which indicates that the energy-saving potential is great. If all Swedish freestanding sulfate pulp-mills became as energy efficient as the most efficient Scandinavian mill, electricity savings corresponding to nearly 1% of the national electricity use would be obtained. In the model mills comparison it was found that large amounts of heat could be saved, particularly in the evaporation plant. [Copyright &y& Elsevier]

Wastewater treatment plants (WWTPs) play a pivotal role in natural water recycling and safeguarding the water security of approximately 42% of the world's population, yet they confront challenges posed by escalating energy consumption and regional energy constraints. In this study, we developed an evaluation framework that integrated regional energy scarcity with the operation conditions of WWTPs to quantify their energy efficiencies by applying multiregional input–output (MRIO) analysis and data envelopment analysis (DEA). A case study using this framework was conducted on 3776 WWTPs in 30 provinces in China. The results showed that the average scarcity-based energy efficiency level of WWTPs in China was low, of which those in northern provinces were higher compared to southeastern provinces. Furthermore, only 24 WWTPs in China attained the highest efficiency levels, while the rest had different degrees of input redundancy, indicating an energy-saving potential of 9.05 × 109 kWh. This paper enables the development of optimal efficiency strategies for WWTPs in different provinces considering the potential energy scarcity risks caused by trading activities. • The DEA and MRIO analyses were combined for 3776 WWTPs in China. • Efficiency evaluation index was modified considering energy scarcity levels. • Scarcity-based energy efficiency assessment framework for WWTPs was developed. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic has highlighted the importance of indoor air quality and ventilation to mitigate the spread of respiratory viral infections. Schools, in particular, represent a vulnerable environment with high occupancy rates, prolonged exposure times and often inadequately ventilated rooms. This paper evaluates the functionality of different natural and retrofitted mechanical ventilation strategies in this context. An experimental setup, combining empirical measurements with building performance simulation and analytical risk analysis was used to assess key performance characteristics, including the energetic performance, thermal comfort, indoor air quality and the airborne infection risk of SARS-CoV-2. The results of this study underscore the need for a holistic approach to ventilation design in schools, taking into consideration the balance between energy performance, carbon emissions, thermal comfort, indoor air quality and associated health factors. We demonstrate that the risk of one or more long-range airborne infections, with the SARS-CoV-2 Omicron variant, can be reduced by >50% through appropriate use of natural, mechanical or hybrid ventilation in a classroom setting. Analytical modelling demonstrates that this risk can be further reduced, by an order of magnitude, through the use of FFP2 masks. • 5 classroom ventilation strategies were evaluated using measured and modelled data. • Mechanical and hybrid ventilation were compared to natural ventilation strategies. • Energy, thermal comfort, IAQ and SARS-CoV-2 infection risk criteria were evaluated. • None of the ventilation scenarios could eliminate the risk of aerosol transmission. • Hybrid systems provide the best compromise solution for all criteria. [ABSTRACT FROM AUTHOR]

• A commuting-pattern-oriented representation of random driving conditions is developed. • Integrated stochastic optimization of powertrain design and control is engineered. • Machine-learning solvers for stochastic optimization are developed. • The stochastic optimization improves the expected energy economy by up to 12%. • Topology modification to EV powertrains improves energy economy by over 17%. The performance of an electric vehicle (EV) powertrain is greatly influenced by the design scheme, control strategy, and driving conditions. Owing to uncertainties in driving conditions and the mutual correlation between design scheme and control strategy, identifying the optimal topology and component parameters of EV powertrains for improving the energy efficiency of EVs remains a challenging task. Oriented to the commuting patterns of individuals, this paper proposes a stochastic optimization method for identifying the superior powertrain design that optimizes the expected energy economy of the EV powertrain and reduces the energy economy variability in random driving conditions. This paper also introduces a machine-learning solution to the complex mutual restrictions among design variables, control strategies, and driving conditions, which determines the feasible design space and powertrain performance. Suggested by massive validations, the stochastic optimization method reduces the expectation and deviation of the energy consumption of EV powertrains up to 12.0% and 5.6%, compared with deterministic optimization methods. Additionally, by comparative evaluation among stochastic optimal design results of different topologies, contributions of topology modifications to the energy efficiency of EV powertrains are clarified, and the superior powertrain topology is identified, which improves 17.1% expected energy economy compared with the current popular topology. [ABSTRACT FROM AUTHOR]

Abstract: A preliminary bottom-up analysis of the energy use in the chemical industry has been performed, using a model containing datasets on production processes for 52 of the most important bulk chemicals as well as production volumes for these chemicals. The processes analysed are shown to cover between 70% and 100% of the total energy use in the chemical sector. Energy use and the heat effects of the reactions taking place are separately quantified. The processes are also compared with energetically-ideal processes following the stoichometric reactions. The comparison shows that there is significant room for process improvements, both in the direction of more selective processes and in the direction of further energy-savings. [Copyright &y& Elsevier]

Highlights • A nonlinear relationship between industrial agglomeration and energy efficiency. • A threshold effect exists when industrial agglomeration affects energy efficiency. • When agglomeration degree is less 1.3865, it will promote energy efficiency. • Future policy for energy efficiency in China's textile industry is suggested. Abstract Based on provincial panel data on the textile industry in China, this paper calculates the total-factor energy efficiency of this industry as the dependent variable. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, in-depth analysis of the industry is performed at different industrial agglomeration levels. The paper identifies different impacts of industrial agglomeration on energy efficiency, uses the threshold regression model to extend the research to a nonlinear framework, and constructs a double threshold regression model in which the threshold of the textile industry agglomeration level serves as the threshold variable. The results show, first, a threshold effect occurs when industrial agglomeration affects total-factor energy efficiency. Second, a significant positive correlation exists among the degree of economic development, energy prices, research and development investment (R&D), enterprise scale, and total factor energy efficiency of the textile industry. Third, a non-linear relationship exists between industrial agglomeration and energy efficiency in the industry. When industrial agglomeration is low, promoting it improves energy efficiency. However, when industrial agglomeration reaches a certain level, agglomeration and energy efficiency show a negative relationship. Finally, based on the empirical results, ways of improving energy efficiency in the industry are suggested. [ABSTRACT FROM AUTHOR]

Highlights • A novel PMSM demagnetization and efficiency estimation framework is established. • The efficiency reduction pattern under different demagnetization levels is analyzed. • The impact of demagnetization on the powertrains’ energy efficiencies is evaluated. • A comprehensive comparative study of the energy efficiency is conducted. Abstract Due to possible overheat, abrasion or mechanical vibrations, demagnetization fault is inevitable in permanent magnet synchronous motors (PMSMs), which could greatly decrease the motor’s efficiency and hence an electrified vehicle’s performance. This paper, from an energy efficiency point of view, proposes to analyze the tolerance ability of different electrified vehicles on motor demagnetization faults, via PMSM flux density degradation modeling, efficiency estimation and dynamic programming (DP) based powertrain energy management. The relationship between different demagnetization levels and resultant motor efficiencies is obtained, and analyzed according to the motor operation area. Demagnetized PMSM is adopted in a pure electric vehicle (PEV), a hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) for energy efficiency analysis. Tolerance analysis indicates that the powertrain efficiency decrease caused by motor demagnetization is more severe under urban driving conditions, especially with PEV and PHEV configurations compared with HEV. A demagnetization threshold investigation is also given in this paper. [ABSTRACT FROM AUTHOR]

Highlights • The paper addresses the topic of Compressed Air Systems (CAS) benchmarking. • CAS represent up to 10% of the industrial energy needs. • The research is a direct result of the European Directive 2012/27/EU. • A data analysis focused on CAS has been carried out in energy-intensive companies. • A benchmarking system has then been designed. Abstract Compressed Air Systems (CAS) are one of the most common and energy intensive utilities in industry, representing up to 10% of the industrial energy needs. Thus, they can be considered as one of the main target systems while planning energy efficiency actions implementation in industry. Nevertheless, most of the available benchmarks of energy consumption are based on nominal data, thus referring to the quality of the design, while benchmarks based on measurements under actual operating conditions are not yet available. In accordance with the Italian transposition of the European Directive 2012/27/EU (i.e. Legislative Decree 102/2014), large and energy-intensive business organizations must perform energy audits at least every four years starting in 2015 and the Italian Energy, New Technology and Environment Agency (ENEA) has the task of collecting and analysing them. In this context, a data collection focused on CAS has been carried out and the subsequent data analysis, focused on nine most promising industrial sectors, has allowed to assess the current state of the art of CAS’s energy efficiency in Italy and to design a possible benchmarking system. Such a system is composed of a set of indicators and a benchmarking visual tool in the form of a matrix. In particular, results related to the Manufacturing of basic metals will be presented for sake of brevity. In this paper we present the results obtained from both the data analysis and the first application of the proposed benchmarking system, together with next steps for the creation of reliable sectorial and cross-sectorial benchmarks. [ABSTRACT FROM AUTHOR]

While technology advancements are improving the energy efficiency of buildings, occupant behavior remains a critical factor in ensuring the effectiveness of such enhancements. To this end, numerous eco-feedback systems have been developed to reduce building energy use through influencing occupants' behaviors during building operations. Information representation is a critical component in eco-feedback systems, affecting the users' interpretation, engagement, and motivation to reduce energy consumption. Many studies have focused on using different charts and technical units or abstract and artistic visualizations to represent energy consumption. However, the effectiveness of such techniques varies across studies. Recent research emphasizes the need to integrate information representation strategies that balance numeric and aesthetic appeal. Concurrently, studies have called for increased adoption of a Building Information Model (BIM) during a building's operations phase to improve facility management. In this paper, we introduce a new eco-feedback information representation method that combines numeric and aesthetic appeal through leveraging spatial and color-coding techniques in BIM. The BIM-integrated energy visualization approach developed in this paper uses the Revit Application Program Interface (API) and allows users to visualize and compare energy consumption values in 2D and 3D views of a multi-family building through a color-coding scheme in an as-built BIM. The method is validated through a user survey that quantitatively and qualitatively assesses the proposed 2D and 3D BIM eco-feedback compared to more traditional bar chart based eco-feedback. Our findings suggest that 2D spatial, color-coded eco-feedback provides the optimal information representation, as it is easy to understand, while evoking engaging and motivating responses from users. This study advances our understanding of eco-feedback information representation while expanding BIM applications during building operations. These are important steps to address the human dimension of energy efficiency in the built environment. [ABSTRACT FROM AUTHOR]

This paper presents the unique design and processing conditions of a micro-CAES device that generates, stores and delivers electrical energy with highly efficient total energy conversion. The micro-CAES installation is divided into six parts, where air, the energy carrier, is compressed, stored, heated and expanded in appropriate sections. We focus mainly on the post-preparation section, where the air mass flow is pulse heated and controlled by Pulse Width Modulation (PWM), and the expansion section, where multiple piston drives are used to achieve maximum energy conversion efficiency or the required amount of electricity to be fed into the grid. We discover that the controllability of expansion process performance is improved by exploiting the inertia of the following elements in multiple reciprocating engines: piston, piston rod, connecting rod, crank, and crankshaft, connected with air injection dynamics and pulse heating by Thermal Energy Storage (TES). Furthermore, we investigate the efficiency using our own zero-dimensional mathematical model illustrating the complex dynamics of air processing. The mathematical model was compared with experimental data for the single-drive air expander, showing qualitatively and quantitatively satisfying model reliability. Moreover, we performed a sensitivity analysis to determine the best number of reciprocating drives involved in the expansion section and the best operating conditions. The use of the PWM function of mass flow of air supplied the expander with an index of 0.5 together with pulsating air heating in the expander installation, including three- reciprocating drives, allows a maximum total energy conversion efficiency of 81%. The presented piston drive expander seems to be a promising solution for micro-CAES in residential and industrial applications, especially where additional low-temperature waste heat is available, and an intermittent control strategy is implemented. [Display omitted] • Original construction of the air expander includes multi-piston-drives. • The micro-CAES with air preheat and PWM control strategy ware implemented. • Obtained maximum efficiency of piston-drive micro-CAES is 81%. • Generated electrical power could be control with overall efficiency change up to 10%. [ABSTRACT FROM AUTHOR]

• Allowing for energy-efficient controls adaptive to diverse production disturbances. • Modeling the system dynamics of serial production lines using max-plus algebra. • Designing an adaptive energy-efficient control policy based on opportunity windows. • The diversity of production disturbances significantly affects energy waste. • Achieving more energy savings with less throughput loss than other control methods. The energy-efficient control has emerged as one of the most promising measures to reduce the unproductive energy consumption of machines during production disruptions. Diverse production disturbances (PDs) are the fundamental cause of production interruptions that result in unproductive energy consumption. In this scenario, the diversity and randomness of PDs necessitate elaborative consideration in energy-efficient controls. This consideration is crucial to the effectiveness of energy-efficient controls, yet it has been generally overlooked. To this end, this paper presents an adaptive energy-efficient control (AEC) methodology to generate adaptive control decisions when machines encounter diverse PDs in serial production lines. As a first step towards AEC, a PD identification method driven by Markov Logic Network is introduced to identify ongoing PDs. To then estimate the system performances in terms of energy savings and throughputs, a system dynamics model for serial production lines is established based on max-plus algebra. Finally, an AEC policy is formulated to optimize unproductive energy consumption on a nearly global scale while maintaining throughputs. A case study was performed on a die-casting production line in order to validate the effectiveness of the methodology. The results indicated a 96.1% F 1 score for PD identifications. Based on this, the AEC policy resulted in a 61.12% reduction in unproductive energy consumption, with a corresponding decrease in throughputs of only 0.65%. It was confirmed that the AEC methodology achieved greater energy savings while minimizing throughput losses compared to conventional energy-efficient control methods. [ABSTRACT FROM AUTHOR]

Biogas is a renewable energy source that can be used either directly or through various pathways (e.g. upgrading to bio-methane, use in a fuel cell or conversion to liquid fuels) for heat, electricity generation or mechanical energy for transport. However, although there are various options for biogas utilization, there is limited guidance in the literature on the selection of the optimum route, and comparison between studies is difficult due to the use of different analytical frameworks. The aim of this paper was to fill that knowledge gap and to develop a consistent framework for analysing biogas-to-energy exploitation routes. The paper evaluated 49 biogas-to-energy routes using a consistent life cycle analysis method focusing on energy efficiency as the chosen crtierion. Energy efficiencies varied between 8% and 54% for electricity generation; 16% and 83% for heat; 18% and 90% for electricity and heat; and 4% and 18% for transport. Direct use of biogas has the highest efficiencies, but the use of this fuel is typically limited to sites co-located with the anaerobic digestion facility, limiting available markets and applications. Liquid fuels have the advantage of versatility, but the results show consistently low efficiencies across all routes and applications. The energy efficiency of bio-methane routes competes well with biogas and comes with the advantage that it is more easily transported and used in a wide variety of applications. The results were also compared with fossil fuels and discussed in the context of national policies. This research resulted in the development of a flexible framework for comparing energy efficiencies which can provide the basis for further research on optimizing the sustainability of biogas-to-energy systems across a range of indicators. [ABSTRACT FROM AUTHOR]

• A method for mapping energy/resource flows of manufacturing systems is proposed. • Key performance indicators highlighting the inefficiencies are developed. • The method supports defining corrective actions to improve production efficiency. • Case study results show a significant saving of energy and resources. The assessment and monitoring of energy and resource efficiency is an essential activity toward the implementation of sustainable manufacturing practices. Existing energy/resource assessment methods and tools are not based on a comprehensive approach, lack on the use of specific key performance indicators, are dedicated to expert stakeholders and do not provide useful suggestions for improving production systems. This paper proposes an innovative method, called Resources Value Mapping that aims to map and classify activities and related energy/resource consumptions according to lean philosophy principles (value-added, non value-added, waste). A user-friendly map and two efficiency indicators (Cost Index and Muda Index) are proposed to quantitatively support the identification of criticalities related to activities, processes, lines, plants, etc., and to successively guide the decision-making process during the improvement strategies implementation. The method has been used to analyze a manufacturing plant that produces cooking appliances. The case study demonstrated the applicability of the method in real industrial contexts and its effectiveness in identifying the energy/resource flows (electricity and compressed air), departments (sheet department) and lines (mechanical and hydraulic presses) for which the waste and non value-added consumptions are prominent. The analysis highlighted that less of 20% of the resources consumed during the process creates value, offering wide margins for improvement. Finally, it aided the definition of an action plan leading to relevant reduction of resource consumptions, economic savings and environmental benefits. [ABSTRACT FROM AUTHOR]

This paper presents hybrid control approaches for heating air supply in response to changes in demand by using the Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) fitting models. Since early 2000’s, some advanced computing and statistical tools were introduced to replace conventional control models in improving control and energy efficiency. Among the tools, the FIS and ANN algorithms were used to define complex interactions between inputs and outputs, and were able to facilitate control models to predict or evaluate precise thermal performance. This paper introduces the FIS and ANN control schemes for simultaneously controlling the amount of supply air and its temperature. Input and output data derived from the FIS results generate and validate the ANN model, and both models are compared to the typical thermostat on/off baseline control to evaluate conditions of supply air for a heating season. The differences between the set-point and actual room temperature and their sums indicate control efficiency, and the heat gains into a room and their sums define the energy consumption level. This paper concludes that the simultaneous control of mass and temperature maintains the desired room temperature in a highly efficient manner. Sensitive controls may have a disadvantage in terms of energy consumption, but the ANN controller can minimize energy consumption in comparison with simple thermostat on/off controller. The results also confirm the effectiveness of simultaneous control of mass and temperature using an ANN algorithm corresponding to intermittent or unpredicted changes in thermal demands. [ABSTRACT FROM AUTHOR]

In this paper, an analytical approach that deals with the optimal sizing of energy storage systems in direct current networks is proposed. In modern power systems, the widespread use of power electronics, storage devices, and automation is driving power engineers to focus on the use of direct current networks. This new focus requires specific tools for the optimal planning and operation of these networks in order to increase energy efficiency and reduce operating costs. This paper is focused on the improvements in the efficiencies of direct current networks, which are characterized by the presence of loads, units for the generation of renewable power, and storage devices. Based on the calculus of variations, an original matrix formulation which starts with the nodal representation of the direct current network is proposed. Two attractive closed-form solutions are presented for minimizing power losses, i.e., (1) a solution based on the approximation of considering the voltage constant at all the network’s busses and (2) a solution based on the linear approximation of the load flow. In both cases, the goal is to minimize losses over a given time horizon (e.g., the daily cycle). The formulation of the problem allows an analytical solution to be obtained that represents a suitable tool for the purpose of designing storage. In addition, the proposed approach can be applied and extended to the optimal sizing of storage systems. The proposed sizing procedure, which uses an analytical approach, is formulated in a general manner that can be used for various storage technologies. The results of numerical applications clearly have demonstrated both the feasibility and accuracy of the methodology to be used in the proposed design. We also propose an interesting parametric study in order to determine the optimal technology and the optimal size of the storage device. [ABSTRACT FROM AUTHOR]

South East Europe is the region in a part of Europe with approximately 65.5 million inhabitants, making up 8.9% of Europe’s total population. The countries concerned have distinct geographical features, various climates and significant differences in gross domestic product per capita, so the integration of their energy systems is considered to be a challenging task. Large differences between energy mixes, still largely dominated by fossil-fuel consumption, make this task even more demanding. This paper presents the transition steps to a 100% renewable energy system which need to be carried out until the year 2050 in order to achieve zero carbon energy society. Novelty of this paper compared to other papers with similar research goals is the assumed sustainable use of biomass in the 100% renewable energy system of the region considered. It is important to emphasize here that only the sustainable use of biomass can be considered carbon-neutral. The resulting biomass consumption of the model was 725.94 PJ for the entire region, which is in line with the biomass potential of the region. Modelling the zero-carbon energy system was carried out using the smart energy system concept, together with its main integration pillars, i.e. power-to-heat and power-to-gas technologies. The resulting power generation mix shows that a wide variety of energy sources need to be utilized and no single energy source has more than a 30% share, which also increases the security of supply. Wind turbines and photovoltaics are the main technologies with shares of 28.9% and 22.5%, followed by hydro power, concentrated solar power, biomass (mainly used in cogeneration units) and geothermal energy sources. To keep the biomass consumption within the sustainability limits, there is a need for some type of synthetic fuel in the transportation sector. Nevertheless, achieving 100% renewable energy system also promises to be financially beneficial, as the total calculated annual socio-economic cost of the region is approximately 20 billion euros lower in the year 2050 than in the base year. Finally, energy efficiency measures will play an important role in the transition to the zero-carbon energy society: the model shows that primary energy supply will be 50.9% lower than in the base year. [ABSTRACT FROM AUTHOR]

Despite all improvements in buildings shell, equipment, and design, CO 2 emissions from buildings are increasing. Since occupants spend more than 87% of their time indoors, they are inseparable and significant elements of building system dynamics. Hence, there is a great potential for energy efficiency in buildings using a wide range of programs such as intervention and eco-feedback. Despite the high level of individual differences and intra-class variability of occupants’ behaviors, the current state-of-the-art eco-feedback programs treat all the occupants uniformly and do not target and tailor the feedback. Therefore, it leaves an opportunity to increase the efficacy of eco-feedback systems through the designing of tailored and targeted programs. In this paper, we conducted a comprehensive analysis and tested hypotheses on occupants’ behavioral responses to a normative comparison feedback program, in addition to the impact of notifications on the level of engagement of each group of occupants. We categorized occupants who participated in the normative comparison feedback program into three groups (i.e. low, medium, and high energy consumers) based on their baseline energy consumption, and tested 9 hypotheses. A mixed-effect regression model (MRM) and a paired t -test was implemented to evaluate the proposed hypotheses. The hypotheses examine the variability of occupants’ responses under the same eco-feedback program, and the effectiveness of notifications on reinforcing occupants’ engagement in these programs. The contribution of this paper is two-fold: (1) reporting that the effectiveness of the notifications in eco-feedback programs are initially highly dependent on the type and the nature of the program, and then the interval and the content of the notification, and (2) demonstrating the variability of occupants’ behavioral responses under the same normative comparison eco-feedback program. These findings indicate the need for a shift in focus toward targeted and tailored feedback programs which treat occupants based on their characteristics. Moreover, they highlight the need for eco-feedback design, development, testing and implementation research that acknowledges and addresses differences in occupant responses to feedback. [ABSTRACT FROM AUTHOR]

The economic assessment of low-carbon energy options is the primary step towards the design of policy portfolios to foster the green energy economy. However, today these assessments often fall short of including important determinants of the overall cost-benefit balance of such options by not including indirect costs and benefits, even though these can be game-changing. This is often due to the lack of adequate methodologies. The purpose of this paper is to provide a comprehensive account of the key methodological challenges to the assessment of the multiple impacts of energy options, and an initial menu of potential solutions to address these challenges. The paper first provides evidence for the importance of the multiple impacts of energy actions in the assessment of low-carbon options. The paper identifies a few key challenges to the evaluation of the co-impacts of low-carbon options and demonstrates that these are more complex for co-impacts than for the direct ones. Such challenges include several layers of additionality, high context dependency, and accounting for distributional effects. The paper continues by identifying the key challenges to the aggregation of multiple impacts including the risks of overcounting while taking into account the multitude of interactions among the various co-impacts. The paper proposes an analytical framework that can help address these and frame a systematic assessment of the multiple impacts. [ABSTRACT FROM AUTHOR]

Energy efficiency policies play a key role in the transformation to a ‘green energy economy’. In this paper, we take stock of the impacts of the existing energy efficiency policy instruments in Germany and review the energy, environmental and socioeconomic impacts of the country’s latest energy efficiency and climate strategies for the year 2020. We find evidence supporting the findings of other studies that enhanced green energy policies will trigger tangible economic benefits in terms of GDP growth and new jobs even in the short term. Whereas policy makers have already acknowledged and implemented this conclusion in the case of renewable energies, our paper shows that striving for more ambitious energy efficiency policies represents a similar win–win strategy, which should be exploited to a much larger extent. [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]

This paper discusses the application of distributed energy resources (DERs) at water resource recovery facilities (WRRF). The DERs considered include solar photovoltaic (PV) systems, biomass electricity generation, and energy storage systems (ESS). The financial and demand management implications of the deployment of various DER combinations are analyzed. The load profile of a single plant is studied in order to draw conclusions regarding the optimal size for energy storage solutions based on the goals of the WRRF. A case study of a Medium sized WRRF, located in a large metropolitan area, is used to compare different financial and technical implementation strategies. An ideal combination of DERs is determined based on analysis of various scenarios for nine distinct use cases. The overarching goal of this paper is to understand the potential of various combinations of DERs for managing peak demand and resilience at WRRFs. Elements in the financial models are compared to determine the combination that creates a financially viable project while providing tangible benefits in resilience and/or demand management to the WRRF. • Energy efficiency is key for urban regions to combat global warming. • Distributed energy resources (DERs) improve resiliency during power failures. • Energy efficiency is possible through deployment of DERs and better use of biogases • Distributed energy resources (DERs) improve resiliency during power failures. [ABSTRACT FROM AUTHOR]

• We developed a nodal model, validated by experiments, of a triple-glazed airflow window. • Airflow window performances have been compared to those of conventional windows. • Heating needs of a room equipped with the airflow window are reduced from 5 to 90%. • Energy balance is neutral for an airflow window while negative for conventional ones. • Airflow window outperforms conventional windows in terms of U-values and g-values. In this paper, we numerically investigate thermal characteristics and performances of a triple-glazed airflow window, by comparison to conventional double and triple-glazed ones. Numerical simulations were performed with a nodal model of the airflow window, which has been first validated from experiments. Both experimental and numerical results show that the air supply preheating through the airflow window is significant, ranging from approximately 10–20 °C without solar radiation and during sunny periods, respectively. In the first case, the energy gain comes from both solar radiation and heat loss recovery through the window, while only from heat loss recovery in the second one. Then, additional simulations have been performed in order to compare the airflow window to a conventional airflow ventilation system combined to conventional double and triple-glazed windows. Numerical results point out that, due to the entering fresh outdoor air, the airflow window causes a stronger cold wall effect. However, at the same time, it reduces cold draught risks (air supply preheating of about 10 °C) and heat losses too. Indeed, for the same reason, the outer face temperature of the airflow window glazing is low and subsequently external heat transfers by convection and radiation reduced. Results also show that, in comparison to conventional windows, energy needs to heat a room are lower if this one is equipped with an airflow window. The reduction approximately ranges from 5% for night-time to 90% for daylight conditions. The main reason is the significant reduction in energy needs to heat the fresh air entering the room, by 36% and 79%, respectively. For an entire day, we found that the energy balance sheet is approximately neutral for an airflow window (−70 Wh), while negative for conventional double and triple-glazed ones (−572 Wh and −653 Wh, respectively). Finally, we compare and discuss the airflow window performance in terms of thermal transmittances, U g and U w values, and solar heat gain coefficients, g g and g w values. Results obtained for the airflow window point out that U g and U w significantly decrease when the airflow rate increases, due to an increase in the air supply preheating, especially until 15 m3 h−1. Results also show that it outperforms the double-glazed window at any airflow rate and the triple-glazed one for airflow rates from just 3 m3 h−1. At the opposite, g g and g w increase with the airflow rate, especially until 15 m3 h−1 and the comparison points out that the airflow window outperforms both the double and triple-glazed ones at any airflow rate. As an example, for an airflow rate of 15 m3 h−1, that is for practical applications in residential or office buildings, the glazing solar heat gain coefficient of the airflow window is approximately 20 and 30% higher than double and triple-glazed ones, respectively. [ABSTRACT FROM AUTHOR]

Highlights • We estimate the energy rebound effect caused by technological progress in Shanghai. • We use the state space model with time-varying parameter based on the IPAT identity. • Shanghai has a high partial rebound effect on average at the overall-economy level. • Mitigating the energy rebound effect can significantly reduce carbon emissions. • Energy efficiency improvement should not be the only way to conserve energy. Abstract The energy rebound effect is regarded as an obstacle of achieving the expected target of energy-saving policies, especially under a rapid urbanization background in developing counties, such as China. This has become a substantial drag of sustainable development in some cities. Shanghai is the economic center of China, and it is also a typical energy import-dependent mega-city. Investigating the evolution of Shanghai's energy-saving performance and the energy rebound effect is significant for the implementation of energy-saving policies in other similar cities of China and other developing countries. Using the state space model with time-varying parameters and based on the IPAT identity and the Solow residual approach, this paper is the first study to present a specific estimation on Shanghai's energy rebound effect caused by technological progress. The results show that, during the period of 1991–2016, the average energy rebound effect of overall economy and secondary industry in Shanghai was 93.96% and 73.10%, respectively, indicating a high partial rebound effect. Most of expected energy saving caused by improved energy efficiency is offset by extra energy consumption caused by technological progress. Regarding tertiary industry, the average rebound effect was 146.61%, indicating a backfire effect. However, the average energy rebound amount of tertiary industry is less than that of secondary industry. In particular, there is an increasingly negative impact of the rebound effect of tertiary industry on energy conservation in recent years, with the sector's rapid expansion and corresponding increase in energy demand. Furthermore, we estimate the carbon rebound amount (i.e., carbon emissions caused by the energy rebound effect) and find that, on average, the energy rebound effect caused 13.1% and 0.41% increases in carbon emissions in Shanghai and China, respectively. Therefore, mitigating the energy rebound effect can significantly reduce carbon emissions. Due to the substantial impact of the rebound effect, technological progress and energy efficiency improvement should not be the only way to achieve energy-saving target, especially in energy import-dependent mega-cities like Shanghai. Some supporting policies should be implemented to ensure that the expected outcome of energy-saving effort can be realized as far as possible. [ABSTRACT FROM AUTHOR]

• An analytical power-rating method based on techno-economic criterion is proposed. • Optimal power-rated motors work with less energy losses and life-cycle cost. • Techno-economic optimal power-rated motors are 1–2 steps larger than the RMS load. • The proposed method can be used even when the list price is not available. Electric motors are valuable, long-life industrial assets, whose lifespan is often measured in decades. A period of unplanned motor downtime in an industrial plant frequently incurs an expenditure of thousands of euros per hour. As a consequence, plant engineers often focus their decisions on reliability rather than on operating cost (energy bill). Unfortunately, the high price of energy and its growing trend mean that operating cost has an increasingly negative impact on production cost and company competitiveness. The problem of selecting the rated power of a line-operated single-speed electric motor to drive a time-variable mechanical load has traditionally been solved, based on the well-established root mean square (RMS) value of the time-power profile of the mechanical load. This conventional method of rating is strictly technical, since the rated power of the motor is determined, based only on the power profile of the load. No other factors are considered, such as the energy consumption throughout the whole in-service life of the motor, or, even better, the whole life cycle cost (LCC), which is defined as the net present value of purchasing, installing, operating (energy), maintaining and decommissioning the motor throughout its life. As a consequence, conventional rating often leads to the selection of motors with a power rating that is technically sufficient to drive the mechanical load, but insufficient to do so with the lowest possible energy (losses) consumption, or even better, with the lowest possible life cycle cost. A new analytical method to determine the techno-economic optimum power rating of a line-operated single-speed electric motor to drive a time-variable mechanical load will be introduced in this paper. The proposed method takes into account not only the technical restrictions due to the time-power profile of the mechanical load, but also the whole life cycle cost. Based on the list of electric motors offered in the manufacturer's catalogue, the new methodology enables the optimum techno-economic rated power of the motor to be calculated, which minimizes the energy consumption (energy loss) throughout its in-service life or its total life cycle cost. The results show that the optimum techno-economic rating is one or two rated-power levels above the conventional rating based solely on the RMS load. [ABSTRACT FROM AUTHOR]

• The paper illustrates the importance of capturing cross-sectoral relationships between industries in IAMs. • Retrofitting and clinker to cement ratio is not sufficiently represented in IAMs. • Retrofitting can offer considerable energy savings in the short-term. • Reducing the clinker content in cement can offer significant energy savings. • Limited future fly ash availability reduces the potential for clinker substitution. Although the cement industry emits around 6% of global CO 2 emissions, most global Integrated Assessment Models (IAMs) barely represent this industrial subsector or do not cover all important processes. This study, describes the state-of-the-art of cement modelling in IAMs, suggests possible improvements and discusses the impacts of these on energy and greenhouse gas emissions (GHG) in the IMAGE global IAM. It is found that two cement-sector specific GHG mitigation measures are often not explicitly accounted for in IAMs, namely: (i) retrofitting and (ii) reducing the clinker to cement ratio. For retrofitting, many measures are identified as cost-effective and when incorporating these in the IMAGE model overall energy use reduces between 2010 and 2035 by 9.8 and 11 EJ (4% and 5%) under the baseline and GHG mitigation scenarios, respectively. When incorporating the clinker to cement ratio by linking material availability to the activities in the steel industry and coal-fired power plants, the 2050 energy use reduces by 15% under the baseline scenario and increases by 9% under the GHG mitigation scenario as fewer coal-fired power plants are in operation. This is even more prominent in the long term. The 2100 energy use is 14% higher in the GHG mitigation scenario as even fewer coal-fired power plants are used drastically limiting the potential for clinker substitution with fly ash. These results highlight the importance of capturing cross-sectoral relationships between industries and of including sector specific mitigation measures in long-term energy models. [ABSTRACT FROM AUTHOR]

Highlights • Co-simulation and building energy models provide a framework to test control algorithms. • Machine learning algorithms are effective to develop predictive optimisation models. • Predictive controls for residential can reduce heating systems costs up to 40%. • Smart predictive control in buildings results in lower carbon footprint up to 39%. • The smart grid dynamically controls dwellings to balance electricity supply/demand. Abstract This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm). [ABSTRACT FROM AUTHOR]

Highlights • High-temperature packed-bed thermal energy storage of volume 175,000 m3 is studied. • One-dimensional, two-phase transient model is developed. • Parametric study is carried out to analyze the dynamic behavior. • Energy and exergy efficiencies exceeded 98% for most of the cases investigated. Abstract The packed-bed thermal energy storage technology has gained a significant market worldwide as it offers a huge potential for high-temperature air based storage with no adverse environmental impact. The present paper numerically investigates a high-temperature 175,000 m3 truncated conical shaped packed-bed thermal energy storage. One-dimensional, two-phase model is developed to simulate the transient behavior of the storage where the energy balance of both phases, the fluid and the solid, are based on modified equations of the Schumann model. The model developed is used to carry out a parametric study where it is subjected to different design and operating parameters such as storage shape, rock diameter, charge-discharge rate and insulation thickness. The storage performance is assessed through energy and exergy efficiencies taking energy stored and recovered into account. The model satisfactorily predicts the dynamic behavior of a large-scale packed-bed storage system within the range of parameters investigated. Results obtained denote that the truncated conical shaped storage with rock diameter of 3 cm, insulation thickness up to 0.6 m and charging-discharging rate of 553 kg/s leads to lower thermal losses estimated under 1% of the energy recovered during discharge. Furthermore, energy and exergy efficiencies of thermal cycles are found to exceed 98% for most of the selected parameters in the study. The model developed can assist in identifying more advance and cost-effective storage design solutions for a large-scale application. [ABSTRACT FROM AUTHOR]

Highlights • Develops a method to calculate household appliance stock and electricity demand. • Shows forecasting of appliance stock and electricity demand for the next 20 years. • Calculates cost-effectiveness and payback period for appliance replacements. • Shows the impact of new technologies and energy labelling/standard of appliances. • Shows detailed comparison of different scenarios to achieve energy demand reduction. Abstract This paper presents the development and application of a dynamic model which allows to quantify the changes in the number of white goods in stock, the related evolution of energy efficiency as well as the changes/projections of electricity consumption in the next 20 years using data from Switzerland. According to the "reference scenario" based on observed market trends the electricity demand of white goods is expected to decrease by 8% between 2015 and 2035. The analysis shows that this is the combined result of having more energy efficient appliances in the stock, a higher appliance ownership level, and an increased number of dwellings. The "maximum efficiency" scenario based on new technologies shows an electricity saving potential of white goods of 25%. These findings confirm that energy efficiency standards and labelling can be effective instruments for achieving energy and CO 2 emissions reduction targets. The assessment for cost effectiveness indicates the current limited scope for economically viable energy efficiency improvements of white goods, while novel technological solutions are likely to expand the economic energy efficiency potential. Since white goods and their components are mass-produced and traded internationally, similar findings can be expected for other countries with comparable legislation (e.g. EU member states) but country-specific analyses are nevertheless recommended. [ABSTRACT FROM AUTHOR]

Highlights • Numerical proof of concept is provided for a novel dynamic PCM building system. • The system is able to modify the position of PCM with respect to the insulation. • The system can not only be used as thermal barrier but as a cooling supplier system. • Activations of the system and melting PCM temperature were optimized. Abstract A novel concept based on the dynamic use of phase change materials (PCM) in building envelopes is presented in this paper. The concept aims at breaking the main technical barriers that PCM have been dealing with in its application as passive cooling system: (i) solidification process of PCM is limited and (ii) peak cooling load is delayed but mainly discharged indoors. The concept relies on the ability of the system to modify the position of the PCM layer inside the building envelope with respect to the insulation layer. A proof of concept evaluation based on a numerical tool demonstrates the cooling load reduction potential of this technology when implemented in different construction systems. PCM peak melting temperature as well as the daily activations of the system were optimized using a Particle Swarm Optimization (PSO) algorithm. The numerical results indicate that the dynamic system facilitates dramatically the solidification process of PCM, allowing the system to be designed with lower PCM peak melting temperatures. The potential of the system to charge PCM which solidifies at temperatures lower than indoor set point, allows the technology to be used not only as a thermal barrier but as a cooling supplier system. [ABSTRACT FROM AUTHOR]

Highlights • An optimization method for energy production with power transmission and storage. • Two decomposition models: local parametric, and hourly multi-period network models. • Method has high accuracy and fast solution for long-term problems. Abstract This paper develops a model and optimization method for multi-area heat and power production with power transmission and storage. The objective function of the model is to minimize the operating costs of the system. The model can be used both for planning optimal system operation, and for simulating the effects of extended production, transmission and storage capacity. The proposed parametric decomposition method is fast enough to solve problems with a large number of hourly models. The parametric decomposition method works in two phases. First, the problem is decomposed into hourly local energy production models without storages and transmission. Parametric linear programming analysis is applied to these models for determining the optimal marginal operating costs as a function of power production. In the second phase, the optimal marginal cost functions are encoded as a linear transshipment network model including storages and transmission network. The network model is solved using generic sparse linear programming software. The operation of each production plant is determined based on the network solution. The decomposition method was validated by comparing it against an integrated linear programming model. The decomposition method demonstrates good accuracy and solves yearly models up to 30 times faster than the integrated model. [ABSTRACT FROM AUTHOR]

Highlights • A method is presented to engage experts to obtain domain knowledge. • Virtual sensors are created from expert knowledge and sensor data. • Automated approach selects an optimal set of real and virtual sensors. • Machine learning is used to automatically build a data-driven fault detection model. • High fault detection performance can be achieved using only a few virtual sensors. Abstract Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create 'virtual sensors', and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. This paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort. [ABSTRACT FROM AUTHOR]

Highlights • Overview of US Federal Energy Management (FEMP) lighting efficiency program. • Estimated total benefits of the 2017 lighting update from $33 to 217 million USD. • Estimated energy savings 0.33–4.76 TWh for this update. • Total benefits methodology with monetized full fuel cycle greenhouse gas emissions. • Presents the FEMP lighting categorization structure. Abstract Lighting consumes a great deal of electrical energy in buildings, and the U.S. federal government is the largest single energy consumer in the United States. Therefore, the way that the U.S. federal government manages its lighting energy use is of great importance. One important way that energy use is managed is through Federal Energy Management Program (FEMP) energy efficiency requirements. However, there is not much literature on how these requirements are set and how to value the resulting savings. This paper documents the methodology used to develop these for the 2017 FEMP lighting update. A methodology for calculating the total benefits is presented that goes beyond site energy saved, and includes full fuel cycle energy savings, energy cost savings, emissions offset and monetized emissions costs. The results show total benefits of $33 million USD including 475 kilotons of carbon dioxide (CO 2) offset under the low compliance scenario, $104 million USD including 1494 kilotons of CO 2 offset under the medium compliance scenario, and $217 million USD including 3124 kilotons of CO 2 offset under the high compliance scenario. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of possible energy savings in the German chemical industry. • Uncertainty analysis of costs and potentials of energy saving measures. • Evaluation of marginal cost curves of energy savings with uncertainties. • Determination of important model parameters using sensitivity analysis. Abstract In 2014, 19.3% of Germany’s industrial final energy consumption could be allocated to the chemical industry. Energy efficiency measures with focus on the chemical industry could thus contribute to reaching the German goal of reducing greenhouse gas emissions. To achieve this goal, energy planners and industries alike require an overview of the existing energy efficiency measures, their technical potential as well as the costs for realizing this potential. Energy efficiency opportunities are commonly presented in marginal cost curves, which rank these measures according to specific implementation costs. Existing analyses, however, do not take uncertainties in costs and potentials sufficiently into account. The aim of this paper is to create a marginal cost curve of energy efficiency measures for the chemical industry in Germany, while quantifying the uncertainties of the results and identifying the most influential input parameters. The identification of energy efficiency measures and the quantification of the associated technical potentials and costs were identified based on literature data and own assessments. Based on these findings a cost curve was created for the current technical potential. This potential was found to be 24.4 PJ per year, of which 23 PJ had negative lifetime costs. To investigate the uncertainties of these results, Monte Carlo simulations were performed to quantify the standard deviations of the implementation potential and costs. Furthermore, a sensitivity analysis, based on Morris Screening and linear regression, was conducted in order to identify the most influential model input parameters. With the applied approach, it was shown that uncertainties have a non-negligible impact on the final energy saving potential and costs, as well as the shape of marginal cost curves. The standard deviation of the energy saving potential was found to be 3.1 PJ. Furthermore, it is possible to systematically prioritise efforts in refining data. [ABSTRACT FROM AUTHOR]

Highlights • A new approach for design optimization of zero/low energy buildings is proposed. • The approach integrates multi-stage sensitivity analysis and design optimization. • An optimization method for buildings without heating in subtropics is developed. • Concern of winter thermal discomfort affects the selection of key design parameters. • Such concern affects the optimal design of buildings without heating in subtropics. Abstract To reduce building energy use and mitigate CO 2 emissions, zero/low energy buildings have attracted increasing attention. However, the impacts of the main design parameters and optimal design for zero/low energy buildings only provided with cooling in subtropical regions are seldom studied, and the objectives for building performance assessment and design optimization in such particular situations are not addressed sufficiently. In this study, the impacts of the main design parameters and optimal design for zero/low energy buildings in subtropical regions are studied. A holistic approach integrating sensitivity analysis and design optimization is developed for zero/low energy buildings. A new optimization objective is proposed, which considers annual energy consumption and winter thermal discomfort, for buildings without heating provision. A multi-stage sensitivity analysis approach is proposed to identify the key design parameters for design optimization. The key building design parameters are optimized to minimize the optimization objective using the genetic algorithm. A case study is conducted, using the zero carbon building (ZCB) in Hong Kong as a reference building, to illustrate the implementation steps and effectiveness of the proposed approach. This paper presents the identification of the key influential design parameters in the subtropical climate and the design optimization method of zero/low energy buildings as well as the procedures and the results of the case study. [ABSTRACT FROM AUTHOR]

Due to the rapid rise of power consumption of data centers in recent years, much work has been done to develop energy-efficient design, controls and diagnosis of their cooling systems, while the energy system simulation is used as an effective tool. However, existing models of information technology (IT) equipment of data centers cannot well represent the effects of IT equipment design and operation status on the data center cooling demand, and this hinders the development of the energy saving cooling technologies of data centers. To address this issue, this paper introduces a power consumption model of IT equipment in data centers with coefficients and modeling script provided for immediate use in data center energy system simulation. This energy model can be used to simulate energy performance of typical IT equipment in data centers under real-time dynamic operation conditions conveniently and effectively without the need of data other than the specifications of a data center design and IT equipment manuals. Its use with a commonly used building simulation program is demonstrated with a building model of a typical large office in a subtropical area. The results show that the model can represent the change of power consumption of data centers with different IT equipment designs and operation appropriately. [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]

This paper presents a case study on optimal options for distributed energy resource (DER) technologies to reduce greenhouse gas emissions in a large retail shopping centre located in Sydney, Australia. Large retail shopping centres take the largest share of energy consumed by all commercial buildings, and present a strong case for adoption of DER technologies to reduce energy costs and emissions. However, the complexity of optimally designing and operating DER systems has hindered their widespread adoption in practice. This paper examines and demonstrates the value of DER in reducing the carbon footprint of the shopping centre by formulating and solving a multiobjective optimisation problem using the Distributed Energy Resources Customer Adoption Model (DER-CAM) tool. An economic model of the shopping centre is developed in DER-CAM using on-site-specific demand, tariffs, and performance data for each DER technology option available. Four key optimal DER technology investment scenarios are then analysed by comparing: (1) solution trade-offs of costs and emissions, (2) the cost of reduced emissions attained in each investment scenario, and (3) investment benefits with respect to the business-as-usual scenario. The analysis shows that a moderate investment in combined cooling, heat and power (CCHP) technology alone can reduce annual energy costs by 8.5% and carbon dioxide-equivalent emissions by 29.6%. A larger investment in CCHP technology, in conjunction with on-site solar photovoltaic (PV) generation, can deliver nearly 72% reductions in emissions at a 47% increase in total energy costs. The study demonstrates the effectiveness and flexibility of this type of modelling approach for the analysis, design, and ongoing assessment of new DER technologies under changing market conditions, supporting a more robust adoption of DER technologies in the commercial building sector. [ABSTRACT FROM AUTHOR]

Industrial systems management is nowadays increasingly devoted to improve the control of most critical production aspects. In this context, the performance of all the systems involved in production processes have to be measured and analysed in order to get better insights in terms of potential production and quality improvements as well as energy savings. In order to evaluate the performance of a certain system, a possible and effective way is to compare it with data from similar systems, thus to conduct a benchmark analysis. In the area of energy management, although the compressed air system (CAS) is one of the most important and energy consuming services within industrial plants, enterprises often have difficulties understanding and appreciating the entity of potential benefits coming from the improvement of its energy efficiency. The present paper aims at developing a new benchmark analysis for compressed air systems in industrial plants. The proposed methodology starts from the KPIs (Key Performance Indicators) already available in the scientific literature for CASs’ energy performance and is mainly based on the analysis of a huge real dataset collected from over 15,000 energy audits made on a wide range of different companies, all related to produced quantity of compressed air and energy consumed by CASs. Collected data present some limitations and related improvements and corrective actions have been undertaken and are presented in the followings. Data analyses have been followed by complementary surveys regarding compressed air systems’ use, maintenance and monitoring practices performed within several Italian enterprises and aimed at enhancing and creating a more reliable baseline for benchmarking. [ABSTRACT FROM AUTHOR]

Electric clothes dryers consume about 6% of US residential electricity consumption. Using a solid-state technology without refrigerant, thermoelectric (TE) heat pump dryers have the potential to be more efficient than units based on electric resistance and less expensive than units based on vapor compression. This paper presents a steady state TE dryer model, and validates the model against results from an experimental prototype. The system model is composed of a TE heat pump element model coupled with a psychrometric dryer sub-model. Experimental results had energy factors (EFs) of up to 2.95 kg of dry cloth per kWh (6.51 lb c /kWh), with a dry time of 159 min. A faster dry time of 96 min was also achieved at an EF of 2.54 kg c /kWh (5.60 lb c /kWh). The model was able to replicate the experimental results within 5% of EF and 5% of dry time values. The results are used to identify important parameters that affect dryer performance, such as relative humidity of air leaving the drum. [ABSTRACT FROM AUTHOR]

After China launched its “Belt and Road” (BR) initiative, the international community became concerned that it may worsen the environmental performance of the BR countries. Due to a lack of data for empirical testing, this paper addresses this concern through an indirect method and draws the implications of the potential impacts of China’s BR initiative. This method empirically examines the effects of trade integration and regional cooperation, two major functions of the BR initiative, on energy efficiency (EE) convergence, a concept that describes the catching up process of EE across countries. A sample of 89 countries was selected to analyse the process of EE convergence from 2000 to 2014. The results indicate that although the gaps in EE among countries around the world become larger after 2010, regional cooperation may lead to a convergence process. It also finds that trade integration has a positive influence on convergence across the countries, especially among middle- and low-income countries. The results suggest that the BR initiative, through its roles in trade integration and regional cooperation, may promote EE convergence among countries. This is a desirable environmental outcome. This research also provides policy implications for both China and the other BR countries. [ABSTRACT FROM AUTHOR]

Understanding homeowners' renovation decisions is essential for policy and business activity to improve the efficiency of owner-occupied housing stock. This paper develops, validates and applies a novel modelling framework for explaining renovation decisions, with an emphasis on energy-efficiency measures. The framework is tested using quantitative data from a nationally-representative survey of owner-occupied households in the UK (n = 1028). The modelling advances formal representations of renovation decisions by including background conditions of domestic life to which renovating is an adaptive response. Path analysis confirms that three conditions of domestic life are particularly influential on renovation decisions: balancing competing commitments for how space at home is used; signalling identity through homemaking activities; and managing physical vulnerabilities of household members. These conditions of domestic life also capture the influence of property characteristics (age, type) and household characteristics (size, composition, length of tenure) on renovation decisions but with greater descriptive realism. Multivariate probit models are used to provide rigorous, transparent and analytically tractable representations of the full renovation decision process. Model fits to the representative national sample of UK homeowners are good. The modelling shows that renovation intentions emerge initially from certain conditions of domestic life at which point energy efficiency is not a distinctive type of renovation. The modelling also shows clearly that influences on renovation decisions change through the decision process. This has important implications for policy and service providers. Efficiency measures should be bundled into broader types of home improvements, and incentives should target the underlying reasons why homeowners decide to renovate in the first place. [ABSTRACT FROM AUTHOR]

Understanding the energy metabolism of national economies is nowadays crucial for policymakers in order to define effective policies and to properly set energy efficiency targets. Energy accountings based on the traditional Production-based accounting method (PBA) allows to understand how primary energy is directly extracted, traded, transformed and used within each economy. On the other hand, Consumption-based accounting method (CBA) allows to understand the ultimate economic purposes of such energy flows. The information provided by the joint application of these approaches may provide useful and complementary insight on the national energy metabolism, allowing to identify hotspots for potential interventions from both the supply and demand side. This paper reviews PBA and CBA energy accounting methods, presenting a possible way for the joint use of their results, consistently represented by means of one unique Sankey diagram. This will be useful to have a comprehensive insight on the energy metabolism of national economies, supporting analysts and policymakers in the identification of energy efficiency hotspots. The method is applied to South Africa and to the neighbor country of Botswana, based on data provided by IEA energy statistics and the EORA26 Multi-Regional Input-Output model, taking into account non-renewable fossil energy (raw coal, crude oil and natural gas) and considering the reference year 2013. Results suggest that the joint use of PBA and CBA methods may provide useful information on the hidden energy links among national economies, helping analysts and policymakers in defining alternative energy efficiency policies. For this reason, the Authors argue that results of CBA should be provided alongside energy statistics based on the traditional PBA approach. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) is a grid-scale energy storage technology for intermittent energy, as proven by the decades-long successful operation of two existing compressed air energy storage in cavern (CAESC) power plants. Because of the limited availability of salt domes appropriate for CAESC, the more widely available aquifers (compressed air energy storage in aquifers, CAESA) have recently attracted considerable attention as candidates for CAES. An ideal aquifer for CAESA is highly permeable around the well to facilitate easy injection and withdrawal of air, but the high-permeability region is surrounded by low-permeability zones to minimize the loss of injected air and decrease in energy efficiency. However, such ideal geological structures are not always available in nature. Therefore, the potential of creating man-made low-permeability barrier in high-permeability aquifers is very interesting. In this paper, we investigate the feasibility of man-made low-permeability barriers in high-permeability aquifers using the numerical simulator TOUGH2/Gel to calculate the three-component flow (including a miscible gelling liquid). The simulation results show that an expected low-permeability barrier can be created by injecting grout with certain properties, and the altered aquifer performs well for CAESA. Additional sensitivity studies are also performed to reveal the effects of the various factors on the success of the low-permeability barrier creation, including the critical solidification concentration, the scale factor of the time dependence of the grout viscosity, the relative density of the grout, and the volume of the follow-up water injection. The results indicate that, in a horizontal aquifer, low critical solidification concentrations, and small scale factors are generally preferred and the density of grout should be close to that of the in situ water. For the given volume of the injected grout, there is an optimal follow-up water injection that will create the largest storage space without damaging the barrier. These results may help to extend the candidate sites for CAESA and the prospect of large scale energy storage. [ABSTRACT FROM AUTHOR]

This paper presents a real-time optimization method and demonstrates its application to a Proton Exchange Membrane Fuel Cell (PEMFC) system. The optimization function was defined as mix of two performance indicators, the FC net power and the Fuel Consumption Efficiency, using the appropriate weighting coefficients. The weighting coefficients will modify the optimization surface and many extreme could appear on plateau of the optimization surface. The Global Extremum Seeking (GES) algorithm proposed here as real-time optimization method will locate and track the global maximum point and related to this will be established the optimal fueling rates for the PEMFC system under a given load. In this study four strategies will be tested, including the Static Feed-Forward (sFF) control strategy as reference. The optimal operating conditions were sought at different levels of load and the gaps between these four strategies were estimated. For example, in comparison with the PEMFC system controlled by the sFF strategy, the GES operation of the PEMFC system could increase the energy efficiency with 1–2.1%, depending on the FC current level and values used for the weighting coefficients. If the PEMFC system operates under variable load profile, the Fuel Consumption Efficiency could also increase with more than 0.54 W/lpm for GES&LF-based optimization strategy in comparison with the sFF strategy. The effectiveness of GES&LF-based optimization strategy was shown considering a FC Hybrid Power Source under variable load profile. [ABSTRACT FROM AUTHOR]