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The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno's synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT's periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm's effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5 % when compared to conventional multi-level sleep operation and consumes less energy at 75.21 % of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2 % . Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices. [ABSTRACT FROM AUTHOR]

Cooling equipment is widely used in industry, commerce and households. Due to their widespread use, they are responsible for the consumption of a significant amount of electricity. They are subject to degradation and various types of damage. Most often, their energy efficiency decreases and electricity consumption increases. Practice shows that even a specialist service is unable to diagnose damage at an early stage of its development. The paper presents a comparison of continuous monitoring of the temperature of the cooling chamber as a utility standard, with constant monitoring of the temperature of the cooling chamber and electricity consumption of a professional refrigeration cabinet with a built-in condensing unit. The comparative analysis was intended to confirm the thesis about unconscious waste resulting from assessing the correct operation of the device based on limited information. The experiment showed an increase in daily electricity consumption on average by over 30% during the period of unconscious exploitation of the device in a state of failure and an increase in daily electricity consumption on average above 300% during the period of conscious exploitation of the device in a state of failure, but still at an acceptable level of temperature of cooling chamber. [ABSTRACT FROM AUTHOR]

Sustainable smart cities (SSCs) target decarbonisation by optimising energy consumption through the emerging capabilities of technology. Nevertheless, the energy consumption behaviour of end users has the potential to compromise the effectiveness of technological interventions, reflecting the importance of active social engagement in realising decarbonisation goals. Although extensive research exists on energy consumption behaviour, little is known about how technology engagement affects it, the nature of these technologies, and their role in SSC. The paper aims to identify, categorise, and investigate the smart technologies that impact household energy consumption behaviours and their integration into the larger SSC system. Following a systematic review of 60 articles from the Scopus database (2013–2023), the study found 45 smart technologies cited, with 49% affecting efficiency behaviour and 51% affecting curtailment behaviour. While these technologies inform the city administration level in the SSC framework, the role of end users remains unclear, suggesting a technocratic approach. The study proposes the Sustainable Smart City Network to facilitate a grassroots approach, identifying five key domains: government policies, smart technology adoption, smart technology engagement, smart city infrastructure, and urban sustainability. The study provides an original contribution to knowledge by unveiling the key technologies affecting energy consumption behaviour and outlining the pragmatic requirements for achieving decarbonisation through a grassroots approach. [ABSTRACT FROM AUTHOR]

Environmental product declarations (EPD) to EN 15804 provide information about the embodied carbon dioxide of construction products – their life cycle greenhouse gas emissions – alongside reporting the use of renewable and non-renewable primary energy and secondary fuels, among the other environmental indicators. As the number of EPD to EN 15804 increases, they become a useful data resource to consider these impacts. In moving towards a reduction in the embodied carbon of products, it is necessary to use renewable energy resources efficiently to allow the transition to net zero; this is because of the increasing demands on renewable energy to decarbonise industry, transport and domestic energy consumption and the limited capacity to expand renewable generation. This paper reviews published EPD data for structural and reinforcing steels, cement, bricks and structural timber products, and considers, for the cradle to gate 'product' life cycle stage, exploring the relationship of embodied carbon with embodied energy (total energy used), the balance of renewable and non-renewable energy, and the efficient use of energy. It is found, for bricks and timber, that EPD show products that use a greater percentage of renewable energy have higher embodied energy, suggesting a less efficient use of renewable energy for these products. [ABSTRACT FROM AUTHOR]

A simplified life cycle of any product or service consists of three main stages – manufacturing, including obtaining, transformation, combination of raw materials; transportation, as well as packaging; consumption from purchase up to actual use. Energy is not an exception, it is also an item offered by the market in different forms, and everyone in need of it has the right to buy it in case the price is acceptable. Heat power is one of the energy types required by households and important for proper maintenance. Without heating, as well as electricity, modern houses cannot function appropriately, are not attractive to their inhabitants, do not provide comfort and safety. Nevertheless, heat power often is considered as a by-product of electricity production, nowadays a swift price increase for both types of energy is inevitable. Energy efficiency is the level of appropriate usage of energy resources, which can be determined as the relation between final product type, quality, quantity and consumption of energy necessary to create this product [1]. Indirectly energy efficiency impacts health of the society in general and each individual in particular. Renovated energy efficient apartment buildings have a better ventilation system and microclimate. The problem of moisture and mold is at least partly solved, comfort is reached by consuming less energy, especially, heat power [2]. The aim of the paper is to research the impact of different energy efficiency increasing measures on heat consumption in multi-apartment buildings. [ABSTRACT FROM AUTHOR]

This research provides a comprehensive exploration of energy efficiency dynamics in non-residential public buildings such as schools, swimming pools, hospitals, and museums. Recognizing the distinct energy consumption patterns of each building type, the study accentuates the unique challenges they present, with a particular focus on the continuous and intensive energy demands of hospitals and the unparalleled energy needs of swimming pools. Through an extensive review of various case studies, the research unveils prevailing energy consumption trends, highlighting the role of metrics in assessing energy efficiency and the inherent challenges these metrics face in ensuring uniformity and direct comparability. A core element of this analysis emphasizes the dual nature of technical retrofitting, categorizing interventions into passive and active measures. The research delves into the sustainability imperatives of energy interventions, exploring the economic motivations underpinning retrofit decisions, and the intricate relationship between advanced technological solutions and the behavioral tendencies of building operators and users. Additionally, the study uncovers the influence of external determinants such as climatic factors and government policies in shaping energy consumption in public buildings. In synthesizing these findings, the paper offers insightful recommendations, emphasizing the need for an integrated approach that harmonizes technological innovations with informed operational habits, aiming to optimize energy efficiency in public non-residential buildings. [ABSTRACT FROM AUTHOR]

The aim of this study was to measure and model the fuel consumption of cut-to-length (CTL) logging machinery in cutting and forwarding under Nordic harvesting conditions, and to clarify which factors had the greatest effect on the fuel consumption. In addition, the total annual fuel consumption and CO2eq emissions were calculated for CTL logging operations in Finland in 2020. The data were collected during a long-term follow-up study on the fuel consumption of harvesters and forwarders conducted between March 2018 and April 2019. The fuel consumption data were obtained from a total of 16 harvesters and 13 forwarders equipped with digital flow metres featuring an accuracy of ± 1% of the fuel volume measured. The engine power of the forest machines explained most of the hour-based fuel consumption. Correspondingly, the harvesting conditions of the forest stand best explained the cubic-metre-based fuel consumption. The fuel consumption of CTL logging operations (cutting and forwarding) averaged 1.4 L m−3 in final felling, and 3.1 and 2.2 L m−3 in first and later thinning, respectively. There was a large variation in the cubic-metre-based fuel consumption between individual machines, both for harvesters and forwarders. The total calculated fuel consumption in Finnish CTL operations in 2020 was 126.6 million L, with the calculated CO2eq emissions totalling 334,209 t (i.e. 5.7 kg m−3). Several measures to accelerate fuel and energy efficiency and reduce CO2eq emissions in CTL logging operations are discussed in the paper. [ABSTRACT FROM AUTHOR]

Greenhouse gases emission as well as total energy consumption in buildings of public importance, such as schools, municipal buildings, health care centers, can be significantly reduced by increasing buildings' energy efficiency. Buildings' energy consumption adds up to 37% of total energy consumption in the EU countries. In the Republic of Serbia this amount is significantly higher, about 50%. School buildings are considered as one of the most diverse structures from the point of energy-efficient design and construction. The main aim of this paper is to determine the most appropriate settings for possible improvements in energy efficiency and temperature comfort inside a typical primary school classroom in Serbia. The energy efficiency analysis was performed during the heating season for the naturally ventilated primary school classroom located in the eastern Serbia region. The analysis was performed using novel CFD model, suggested in this paper. The suggested model was used to solve two hypothetical scenarios. The first scenario simulates the temperature field in classroom with current energy characteristic envelope of the school building. The calculated numerical data from the first scenario were compared with in-situ measurements values of temperature and wall heat fluxes and showed satisfying accuracy. The second scenario was simulated to indicate possible improvements, which would allow energy consumption decrease and thermal quality enhancement. The analyzed results, calculated using the suggested numerical model under the second scenario conditions, showed that using appropriate set of measures, it is possible to obtain desired temperature comfort levels without need for increase in the building energy consumption. [ABSTRACT FROM AUTHOR]

Purpose: There is a great interest in developing eco-friendly operations as alternative uses of resources in the university campus, making the employment of technologies more sustainable. Practices such as energy management and efficiency initiatives have been encouraged to meet these sustainability goals. The purpose of this paper is to describe and discuss the main features of the GENIIOT project, a system of energy management using Internet of Things applied in a Brazilian university campus to support energy education practices and to promote lasting energy efficiency measures. The focus of the study is directed at the energy consumption of the air-conditioning system, which is responsible for 40% of the total electricity consumption. In addition, this project also scrutinizes processes such as the building occupants' comfort demands, lighting levels and energy waste control. Design/methodology/approach: A case study at the Unicamp campus was conducted to illustrate the design procedures through qualitative description. This study comprises an overview of the physical plant, hardware and software development, energy data monitoring, management process and energy education. Findings: The GENIIOT project enables energy efficiency actions by concerning the use of air-conditioning equipment based on monitored data, different types of monitored rooms, user's behaviors and their feedback. This initiative requires the engagement, awareness and actions from users combined with investment in energy efficiency to achieve an efficient use of this type of equipment. The proposed approach can be applied in the future to similar situations, inside the Unicamp campus and at other university campuses. In addition, this project can contribute to building efficiency analysis by using the hardware structure developed to monitor facilities and carry out evaluations, providing valuable information for strategic initiatives in energy efficiency projects and research and development programs based on practical experience and promoting a discussion about sustainability aspects in the context of the university campus for energy efficiency. Originality/value: The GENIIOT project is aligned with some of the sustainable development goals, among which sustainability and responsible consumption are identified. Considering the sustainability issues, the economic dimension can be evaluated through a cost–benefit analysis of energy efficiency projects with prudent investment, while the social attractiveness of the project is ensured by education and awareness practices for the community that interacts with the system and learns about a more efficient way of using resources in the campus. Furthermore, there is a more conscious use of natural resources by minimizing waste from the use of electricity and reducing carbon dioxide emissions. GENIIOT is part of Sustainable Campus Project, a living laboratory which aims at developing energy-efficiency-related research activities in the university campus to reduce costs in higher and federal institutions of education in Brazil. [ABSTRACT FROM AUTHOR]

The well known cloud computing is being extended by the idea of fog with the computing nodes placed closer to end users to allow for task processing with tighter latency requirements. However, offloading of tasks (from end devices to either the cloud or to the fog nodes) should be designed taking energy consumption for both transmission and computation into account. The task allocation procedure can be challenging considering the high number of arriving tasks with various computational, communication and delay requirements, and the high number of computing nodes with various communication and computing capabilities. In this paper, we propose an optimal task allocation procedure, minimizing consumed energy for a set of users connected wirelessly to a network composed of FN located at AP and CN. We optimize the assignment of AP and computing nodes to offloaded tasks as well as the operating frequencies of FN. The considered problem is formulated as a Mixed-Integer Nonlinear Programming problem. The utilized energy consumption and delay models as well as their parameters, related to both the computation and communication costs, reflect the characteristics of real devices. The obtained results show that it is profitable to split the processing of tasks between multiple FNs and the cloud, often choosing different nodes for transmission and computation. The proposed algorithm manages to find the optimal allocations and outperforms all the considered alternative allocation strategies resulting in the lowest energy consumption and task rejection rate. Moreover, a heuristic algorithm that decouples the optimization of wireless transmission from implemented computations and wired transmission is proposed. It finds the optimal or close-to-optimal solutions for all of the studied scenarios. [ABSTRACT FROM AUTHOR]

While Orthogonal Frequency Division Multiplexing (OFDM) is a dominating spectrum access technology in modern, wideband access networks, it is important to maximize its transmission efficiency considering the underlying radio front-end characteristics. A practical front-end contains nonlinear components, e.g., a Power Amplifier (PA), resulting in nonlinear distortion being injected into OFDM band deteriorating symbols detection. A PA operating point, defined here by Input Back-Off (IBO), can be adjusted to balance the wanted signal power and nonlinear distortion power. While it is the most common to maximize the spectral efficiency (SE), recently, energy efficiency (EE) maximization gained momentum. However, EE maximization requires, in addition to PA nonlinearity modeling, modeling of the power consumption of the PA and all other transmitter components. While it is commonly overlooked, if a battery is used to power the transmitter, its model should be considered as well. This paper derives mathematical expressions for EE and SE of an OFDM transmitter considering Rapp and soft-limiter models of PA nonlinearity, class A, class B, and perfect PA power consumption models, and two battery models: perfect and worst-case. While closed-form expressions cannot be obtained for most of the derived integrals, numerical methods have been used to obtain the optimal IBO value in each case. The numerical results show, in addition to optimal IBO values, the expected Signal-to-Noise and Distortion Ratios (SNDRs). It is shown that the optimal IBO value changes significantly with the wireless channel properties, utilized hardware architecture, or the utilized optimization goal. As such, the proposed optimization is an important topic for 5G and beyond transmitters. [ABSTRACT FROM AUTHOR]

Summary: Wireless visual sensor networks (WVSN) have vital roles in surveillance applications. In these networks, wireless visual sensors include camera and transceiver module and collect visual information. However, energy consumption and coverage of the tracked targets are important challenges in WVSNs since increasing the coverage leads to increasing energy consumption. Therefore, energy optimization and satisfying the quality of experience (QoE) of the tracked targets are essential issues in these networks. In fact, the appropriate focal length setting leads to increasing the target coverage and quality of the captured targets' images and energy consumption. Therefore, selection of the suitable visual sensors and setting their focal length can overcome the energy consumption challenges and improve QoE of the tracked targets. In this case, compressive sensing is also expected to overcome the battery constraints of the WVSN resources. In this paper, the problem is to minimize the energy consumption of the multi‐target tracking with high reliability, while the coverage and also the quality of the received image of the targets are satisfied by selection of the proper visual sensors and the focal length adjustment. The convex optimization method is used to solve the problem. Also, based on the Karush‐Kuhn‐Tucker conditions, the optimal solution for the problem is obtained. Simulation results validate the efficiency of the proposed method in comparison with the other bench mark algorithms. [ABSTRACT FROM AUTHOR]

The quality of human nutrition has acquired significant improvements thanks to the opportunity to store food in suitable temperature conditions. Refrigeration has allowed the slowing of chemical and biological degradation and hence the waste of foodstuff, but at the same time increases energy consumption. These effects impact the environment and the sustainability performance of the cold chain, and drive consumers' choices. The stakeholders of the chain are, therefore, constantly looking for improvement actions to reduce environmental impacts. This paper aims to provide a methodology for prioritizing and assessing the energy efficiency measures for cold chains in terms of quality losses and specific energy consumption, distinguishing between technological, maintenance, and managerial opportunities. This analysis is based on the cold supply chain tool, developed under the H2020 project ICCEE ("Improving Cold Chain Energy Efficiency") which focuses on a holistic approach, not looking only at the individual stages of the cold chain. Furthermore, an economic evaluation has been proposed considering cost savings and the investment needed. [ABSTRACT FROM AUTHOR]

Purpose: Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node. Design/methodology/approach: In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured. Findings: Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods. Originality/value: This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms. [ABSTRACT FROM AUTHOR]

• GPUs may be safely undervoltage, allowing for non-conventional DVFS configurations. • A benchmark suit characterizes GPU components regarding undervoltage limitations. • ALU and DRAM-Cache controller are the most sensitive components to voltage drops. • High energy efficiency gains are obtained with decoupled frequency-voltage pairs. • Accuracy of Deep Neural Networks (DNN) applications is not compromised. The use of GPUs to accelerate DNN training and inference is already widely adopted, allowing for a significant performance increase. However, this performance is usually obtained at the cost of a consequent increase in energy consumption. While several solutions have been proposed to perform voltage-frequency scaling on GPUs, these are still one-dimensional, by simply adjusting the frequency while relying on default voltage settings. To overcome this limitation, this paper introduces a new methodology to fully characterize the impact of non-conventional DVFS on GPUs. The proposed approach was evaluated on two devices, an AMD Vega 10 Frontier Edition and an AMD Radeon 5700XT. When applying this non-conventional DVFS scheme to DNN training, the obtained results show that it is possible to safely decrease the GPU voltage, allowing for a significant reduction of the energy consumption (up to 38%) and of the EDP (up to 41%) on the training procedure of CNN models, with no degradation of the networks accuracy. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

Sustainable production is of the most serious concerns that affect production systems. In a manufacturing company, efficient energy consumption, which leads to significant environmental benefits, is an important factor that indicates the performance of sustainable production. This paper proposes a three-stage data-driven model to analyze energy consumption in production systems. The first stage develops energy consumption predictors, the second stage extracts production scenarios, and the last stage predicts the energy consumption for each scenario. We implemented the model in a steel manufacturing plant for investigating the electricity consumption (EC) of Electric Arc Furnace (EAF). First, we developed four groups of predictors where Boosted Neural Network achieved the best result in predicting EAF's electricity consumption (RMSE = 587, R-Squared = 0.859, MAPE = 0.073). Second, we extracted eight distinct production scenarios based on different amounts of input materials through a descriptive data-mining algorithm, K-means. Third, the EC of production scenarios was predicted by the best predictor. Feature analysis showed that Direct Reduced Iron(DRI), ladle age, and scrap grade-3 have the most effect on predicting EC. Scenario analysis illustrated that scenarios with a higher share of DRI cause a higher amount of EC. Contrastingly, input materials with more share of high-grade scrap types lead to more efficient EC. [ABSTRACT FROM AUTHOR]

Even if massive multiple-input multiple-output (MIMO) can theoretically bring huge benefits, it incurs substantial hardware complexity and expensive hardware costs. To address these issues while maintaining the system performance simultaneously, we develop an asymmetrical transceiver architecture for massive MIMO systems in this paper by releasing the shackles on radio frequency (RF) chains. Specifically, we first develop the architecture for the asymmetrical transceiver where the number of receive RF chains is different from that of the transmit RF chains. Due to this unequal number of RF chains, channel inconsistency appears. To overcome the channel inconsistency and thus fully harness the system's downlink data transmission capability, we propose two uplink-to-downlink channel transfer algorithms. The cost and power consumption models for the asymmetrical transceiver are also developed and the uplink signal-to-noise loss due to the channel inconsistency is investigated. Through analyzing system spectral, cost, and energy efficiency, we demonstrate that the proposed asymmetrical transceiver-based massive MIMO system can achieve excellent downlink spectral efficiency while maintaining a reasonable energy efficiency. [ABSTRACT FROM AUTHOR]

China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants' location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants' positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control. [ABSTRACT FROM AUTHOR]

This paper analyses historical energy consumption trends of the EU road transport sector and examines the role of key determinants, such as economic and population growth, fuel prices, passenger and tonne-kilometres and vehicle fleet characteristics. In light of the EU 2030 and 2050 energy and climate targets, the paper assesses the energy efficiency and decarbonisation progress from 2000 to 2018. A detailed analysis is carried out using a balanced panel dataset from 1995 to 2018 and a test for causality between energy consumption and the selected explanatory variables. The results suggest that the declining trend of the normalised energy consumption of cars, trucks and light vehicles, which both dropped significantly in 2018 compared to 2000, can be related to energy efficiency and decarbonisation policies adopted by the EU and its Member States over the studied period. Decomposition analysis based on the Logarithmic Mean Divisia Index technique confirms the role of energy efficiency gains in curbing energy consumption of road transport, even with the strong upward force exerted by motorisation and population effects, especially in passenger transport. It also suggests that during the recession years (2008–2013), road transport consumption fell due to a sharp drop in the mileage but not in the motorisation effect, which continued to drive up consumption even in this period. Despite some positive developments, the increased demand for road transportation has led to continued energy consumption growth. This has been further compounded by differences between real-world and vehicle certification fuel consumption data. The article highlights the need to strengthen the current policy framework at the EU and MS levels in the next decade to reach the long-term decarbonisation targets. In the context of these targets, a more in-depth investigation of the real impact of energy efficiency and decarbonisation efforts will also become increasingly important. [ABSTRACT FROM AUTHOR]

• Design of an ACTR-based aerial work platform based on the energy efficiency of UAVs. • A power consumption model for UAVs towing cables to generate a certain wrench. • Position of UAVs and tension distribution of cables for optimal energy efficiency. • Number of UAVs for optimal energy efficiency. Aerial Cable Towed Robots (ACTRs) have been proposed by several researchers for aerial manipulation in recent years. However, the low energy efficiency of the multi-rotor Unmanned Aerial Vehicles (UAVs) of ACTRs prevents ACTR-based aerial work platforms for aerial manipulation from a wider range of applications. To provide a guideline for the design of an ACTR-based aerial work platform with an improved energy efficiency of UAVs, this paper investigates the effects of the position of UAVs, the tension distribution of cables driven by UAVs, and the number of UAVs on the energy efficiency of the UAVs of an ACTR-based aerial work platform. A power consumption model is developed for UAVs, assuming that the UAVs are towing cables to stably generate a certain resultant wrench or force on the moving platform. Based on the developed power consumption model, the position of UAVs and the tension distribution of cables to obtain the minimum total power consumption of UAVs are determined. Then, this paper studies whether an additional UAV will inevitably increase the minimum total power consumption of UAVs. The achievements of the energy efficiency of UAVs in theory are explained in case studies. [ABSTRACT FROM AUTHOR]

• The thermo-injection masonry wall drying method was studied. • The behavior of wall samples during drying in a semi-laboratory scale was investigated. • Energy consumption during drying with different parameters was investigated. • The lowest amount of energy was consumed for drying without heating. • Drying with heating showed high moisture removing efficiency and energy consumption. • Two-phase drying showed a good perspective for the optimization of the process. Excessive wall dampness causes damage to buildings, increases energy consumption for heating and cooling, and favors breeding microorganisms dangerous for people's health. In that case, moist walls must be dried and renovated, including making them waterproof. However, present methods for drying are highly time- and energy-consuming and its optimal implementation should be searched. This paper investigates the behavior of wet masonry wall specimens during the drying process in the semi-laboratory scale. The study aimed to examine the influence of different operating parameters of the considered masonry wall drying method on samples' drying rates and propose an optimal drying strategy. Small-scale masonry wall samples made of red ceramic bricks, cement-lime binder, and plaster were considered. Specimens had similar dimensions of approximately 1000×1000×600 mm and a dry mass of roughly 1050–1150 kg. Two types of constructions were studied, i.e., plastered and unplastered. The drying process was conducted by applying the thermo-injection method and using a dedicated device that ventilated and heated the wet sample. Different drying device operating conditions and drying process implementations were tested, i.e., single-phase drying without and with heating for two constant mass flow rates of drying air and two-phase drying initially without and then with heating and with two different mass flow rates of drying air. Moisture mass change in the wet specimen, temperatures in several locations in the sample, relative humidity and temperature of the surrounding air in the room, as well as the drying device electrical parameters, were monitored to have insight into the drying phenomenon and assess the energy efficiency of the process. In both tested constructions, the lowest amount of energy was consumed during drying with ventilation mode on and without heating. However, much less moisture was removed from samples in these implementations than for drying implementations with heating. The drying with heating mode activated showed the highest moisture removal efficiency, but energy consumption increased significantly. The drying strategy with a two-phase drying process, i.e., initially without and later with heating of the specimen, showed promising efficiency and a good perspective for the future optimization of the process. [ABSTRACT FROM AUTHOR]

The ability to recycle phosphorus (P) components in reusable strength from wastewaters presents a promising opportunity to meet the increasing consumption demand. In this paper, the concentration of a dilute stream of phosphoric acid from 15 to 60 wt% was demonstrated using a high-performing PE separator in direct contact membrane distillation (DCMD), benchmarking against commercial PVDF and PTFE membranes. Combining with morphological/structural/material characterization, the membrane performance and energy efficiency were analyzed in-depth based on a combined heat and mass transfer model, which coupled the effects of both concentration and temperature polarization. The interplay of membrane flux, operating conditions and energy indicators was examined, indicating the significant influence of feed temperature, membrane characteristics and the trade-off relationship between Evaporation Efficiency/ Gained Output Ratio and Specific Energy Consumption. The concentration of 15 wt% H 3 PO 4 stream using the PE separator was operated continuously for 100 h with minimal change in flux (> 40 kg/m2·h at 65 °C). The long-time stability was confirmed by membrane autopsy before and after experiments. The results demonstrated the PE separator as a promising low-cost membrane for acid concentration, providing an alternative for resource recovery from harsh solutions. • Systematic study of membrane distillation for concentrating phosphoric acid • Polythene separator had highest flux compared to commercial PVDF and PTFE membranes. • A verified heat/mass transfer model enabled accurate analysis on process efficiency. • Long-term performance revealed stable performance of the low-cost PE separator in MD. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi -level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation. • A real-time PCC system was developed to minimize the energy consumption of EVs. • Two scenarios of car-following and signal anticipation were evaluated. • An ANN model was developed to predict the energy economy performance of the EV. • The proposed PCC can realize 8.5%–15.6% of the energy saving in the urban areas. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

This paper focuses on an unmanned aerial vehicle (UAV) assisted hybrid free-space optical (FSO)/radio frequency (RF) communication system. Considering the rate imbalance between the FSO and RF links, a buffer is employed at the UAV. Initially, theoretical models of energy consumption and throughput are obtained for the hybrid system. Based on these models, the theoretical expression of the energy efficiency is derived. Then, a nonconvex trajectory optimization problem is formulated by maximizing the energy efficiency of the hybrid system under the buffer constraint, velocity constraint, acceleration constraint, start–end position constraint, and start–end velocity constraint. By using the sequential convex optimization and first-order Taylor approximation, the nonconvex problem is transformed into a convex one. An iterative algorithm is proposed to solve the problem. Numerical results verify the efficiency of the proposed algorithm and also show the effects of buffer size on a UAV's trajectory. [ABSTRACT FROM AUTHOR]

• We estimate the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. • The average value of efficiency is 0.491, and the average rebound effect is 48%. • We find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. • The rebound effect varies across before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. The rebound effect is a potential threat to energy efficiency related policies to save energy. Based on data from China family panel studies over the period 2010–2018, this paper measures the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. The result shows that the average value of efficiency is 0.491, and the average rebound effect is 48%. It means that there is still room for improvement in China's household electricity consumption efficiency. But the expected electricity- saving after improving efficiency can only achieve 52%. Besides, we find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. Finally, the rebound effect varies between before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. These findings provide some policy implications for the residential electricity pricing system under the background of energy conservation and climate change. [ABSTRACT FROM AUTHOR]