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Water removal during paper manufacturing is of primary importance to production rate and cost efficiency for the pulp and paper industry. It is crucial to develop methods to reduce energy consumption by increasing the percent solids in the paper web entering the dryers from the presses. This research aimed to develop a fundamental understanding of the effect of bio-chemo-mechanical pretreatments on a bleached softwood fiber matrix and evaluate the impact on the percent solids of the paper web after pressing. Experiments included enzymatic, refining, and cationic polymer pretreatments on the bleached softwood pulps, followed by laboratory papermaking and determining the equilibrium moisture content (EMC) after pressing and the pulp and paper properties. The combined effect of mild refining, controlled enzymatic pretreatments, and cationic strength aids proved to enhance the water removal during wet pressing (up to 35 % reduction) and increase paper strength (up to 60 % increase). The results of increased solids after pressing were used to calculate the potential reduction in drying energy during paper manufacturing. Energy savings of around 10 % for paper drying could be achieved through fiber matrix modification by bio-chemo-mechanical pretreatment. Enzymatic pretreatments have previously been conventionally applied before refining as an energy-saving method. However, this research shows that synergistic actions of enzymes added after refining modify the fibers and create the optimal conditions for enhancement in drainage, press dewatering, and paper properties. [Display omitted] • Enzyme-assisted fiber modifications improve press dewatering and paper strength. • Enzyme blends can enhance fiber cell wall flexibility and fibrillation. • Synergistically, enzymes increased ∼35% press dewatering and ∼60% tensile strength. • Enzymes, mild refining, and cationic biopolymers enhance paper process efficiency. • This research introduces alternative methods for decarbonizing the paper industry. [ABSTRACT FROM AUTHOR]

The paper describes a new method for determining the energy efficiency of the life cycles of wind farms by the aggregated data of energy costs. The rationale for the use of aggregated data to determine energy costs during the life cycle of the wind farms is given. The classification of wind turbines and wind farms elements by the parameters and technical characteristics of the elements with subsequent division into groups for which aggregated data of energy costs are determined is given. Within the framework of the method have been developed an algorithm for determining energy costs on the production of elements of wind turbines and wind farms and formulas for their calculation during the life cycle of wind farms. In order to test the method, energy cost was calculated during the life cycle for two wind farms with wind turbines that differ parameters and technical characteristics of the elements and their energy efficiency was determined. It has been shown that the use of a hydrogen storage unit as part of a wind turbine makes it possible to efficiently use energy during down periods and increase the efficiency of the installation by 25–30%. It is noteworthy that a wind-hydrogen farm allows not only to accumulate excess energy during lean periods, but also to save the resource of wind turbines. When the energy reserve in the hydrogen battery reaches close to full, part of the wind turbines of the wind-hydrogen farm can be automatically temporarily stopped in a given order. This technology can provide an extension of the warranty service life for each wind turbine. [ABSTRACT FROM AUTHOR]

Two-step thermochemical cycle (TSTC) is a promising approach for solar-driven H 2 O splitting. However, extremely high reduction temperature (T red) is a key challenge limiting the development of TSTC. Two-step electro-thermochemical cycle (TSEC) has recently emerged to overcome this problem. However, the pros and cons of TSEC and TSTC are under debate. In this paper, a thermodynamic model was developed for studying the thermodynamic fundamentals and the variation rules of energy efficiency of TSEC and TSTC. The thermodynamic mechanisms of TSTC and TSEC were compared using the Gibbs free energy method and the Exergy & energy level analysis method. In comparison to TSTC, TSEC tends to operate at a lower T red , enabling isothermal operation at moderate temperatures (<1473 K) while sustaining a high H 2 O conversion rate. In addition, energy efficiency analysis reveals that the introduction of 0.2 V auxiliary voltage can increase the energy efficiency from 0.25 in TSTC to 0.38 in TSEC, showing the advantage of TSEC for H 2 O splitting in terms of energy efficiency. This study analyses the advantages and disadvantages of TSTC and TSEC for H 2 O splitting, demonstrating a reference for further development of solar-driven H 2 O splitting. • The thermodynamic mechanisms of TSTC and TSEC are compared. • TSEC tends to operate at a lower reduction temperature with a high H 2 O conversion rate. • A thermodynamic model is developed for studying the energy efficiency of TSEC for H 2 O splitting. • 0.2 V auxiliary voltage can increase the energy efficiency from 0.25 to 0.38. [ABSTRACT FROM AUTHOR]

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers. • The required strong capacity expansion for PV and wind is challenging, but feasible. • "Energy efficiency first" benefits both the local energy transition and H2 exports. • Fully renewable power systems require a suitable political framework. • Higher hydrogen exports might help to reduce the specific power system cost. • Fossil capacity expansion must be stopped to avoid lock-ins and higher future costs. [ABSTRACT FROM AUTHOR]

Waste heat is one of the major problems associated with the energy system. This paper discusses an energy, exergy, and emissions approach for the hybrid system by utilizing waste heat to the maximum extent. A thermoelectric generator (TEG) is incorporated to utilize the low-temperature waste heat from a hybrid model of a solid oxide fuel cell (SOFC) and a combined heat and power (CHP) system. SOFC is an electrochemical cell that generates power at high temperatures through an electrochemical reaction. The high-temperature, unutilized fuel leaves the SOFC, which is burned in the afterburner. The afterburner temperature controls the operating temperature of SOFC. Here, three recuperators are used to utilize this high-temperature waste heat to improve the performance of SOFC and simultaneously generate heat power from the water or gas recuperator. TEG is an auxiliary power production technology that converts waste heat into energy. A novel integration of TEG with SOFC–CHP hybrid system to utilize the low temperature waste heat, is employed. The main goal of this study is to analyze a novel SOFC–CHP-TEG hybrid system in terms of its thermodynamics and emissions. A Two stage TEG is integrated with the SOFC–CHP hybrid system. The simulation model of high temperature fuel cell (SOFC) is developed on MATLAB and validated with published results. This study analyses the impact of pressure ratio and afterburner temperature on the performance of SOFC–CHP-TEG. To do this, perform energy and exergy assessments using the principles of the first and second laws of thermodynamics. A novel exergy-based sustainability index with emissions analysis for the proposed SOFC–CHP is presented. The achieved overall energy efficiency is 62.54% at a pressure ratio of 12 and an afterburner temperature of 1000 K. With an increase in pressure ratio and temperature, the level of CO emissions reduces significantly. [Display omitted] • A novel energy exergy for SOFC–CHP-TEG system is presented. • Exergy based performance is compared and achieve 56.62. • Utilization of waste heat via TEG. • Energy efficiency of SOFC–CHP-TEG is 62.54% at rp = 12 and afterburner temperature of 900 K. [ABSTRACT FROM AUTHOR]

Separate sensible and latent cooling (SSLC) is a promising technology for enhancing the energy efficiency of space cooling systems. In this paper, we propose an SSLC configuration consisting of an indirect evaporative cooling (IEC) device and vapor compression cycle (VCC) for handling space sensible load, and a desiccant wheel for handling space latent load. The performance of the proposed configuration is investigated using physics-based or empirical models for each component. A year-round simulation study of the proposed SSLC system is performed for 12 different cities with diverse climatic conditions. This study considers electrical, thermal, or solar energy for desiccant wheel regeneration. Parameters governing the system performance such as the air flowrate ratio in the IEC and the DW, DW regeneration air temperature, and supply temperature are studied. The proposed SSLC configuration is superior to the conventional system in all cities. The enhancement of seasonal coefficient of performance (sCOP) ranges between 50 and 261 %, 67 and 387 %, and 60 and 2072 % with yearly electrical energy savings of 33 and 72 %, 40 and 79 %, and 38 and 95 % for electrical, thermal, and solar energy for regeneration of DW, respectively. [ABSTRACT FROM AUTHOR]

Considering the escalating global warming and its severe consequences, the exploitation of hydrogen, an eco-friendly energy carrier, is gaining great interest. This paper comprehensively reviews the most common hydrogen production, storage, transportation and utilization processes, while their effectiveness is explored from energy, water, economic and environmental perspectives. Their main characteristics, advantages, shortcomings and challenges are discussed as well. The research reveals that the hydrogen's water footprint, energy consumption, carbon emissions, and cost vary significantly depending on the specific technologies used throughout its life cycle, from production to end use. For instance, fossil fuel-based hydrogen production methods, such as steam methane reforming, offer elevated energy performance and cost-effectiveness, but have a significant carbon footprint. In contrast, water electrolysis is environmentally friendly, but requires more water and has lower energy efficiency. Besides, high-pressure containers are the preferred method for hydrogen storage, while liquid and solid storages are better suited for high-energy-density applications. Regarding hydrogen transportation, pipelines are effective over long distances, whereas tankers are convenient for short distances. Furthermore, fuel cells are more energy, carbon and water efficient when using hydrogen as fuel, but they are generally more expensive than internal combustion engines. [Display omitted] • Hydrogen production, storage, transportation and utilization methods are reviewed. • Their energy efficiency, water use, cost, and environmental impact are explored. • Key recommendations to boost the utilization of hydrogen systems are outlined. • Fossil fuel-based technologies are more energy-efficient and cost-effective. • Renewable-based processes are more environmentally friendly but water-intensive. [ABSTRACT FROM AUTHOR]

The interest in nonthermal discharge plasma has recently increased over the last few decades due to the ability to generate new radicals at atmospheric pressure. This paper presents a comprehensive analysis of the dissociation processes and reaction rates of various water vapour samples by dielectric barrier discharge plasma (DBD). The influence of water contamination and the presence of ammonia (NH 3) on hydrogen production from water vapour was investigated. A novel reaction rate expression was proposed to fit the plasma-water vapour experiment results. The hydrogen production and conversion rate of pure reverse osmosis (RO) water samples showed greater results than the contaminated water samples in all experiments at different tested bubbling temperatures. The higher conversion rate was obtained from RO water combined with 1%NH 3 at a bubbling temperature of 50 °C and was 1.17%. The water contamination influenced the generated DBD plasma micro discharges, consequently decreasing the electron collisions, leading to lower water vapour conversion into constituent elements H 2 and O 2. It was also found that the influence of ammonia on the water vapour decomposition was related to the NH 3 concentration. Moreover, the reaction rate showed good agreement with the experimental results. It was observed that the energy efficiencies were obtained at an operating temperature of 50 °C for pure RO, GTW, and CTW water samples enhanced by adding 1%NH 3 , for example, the RO energy efficiency value at a bubbling temperature of 50 °C increased from 0.133% to 1.15% because of more hydrogen was generated. It implies that the water vapour decomposition processes are influenced by the following parameters: input gas temperature, reaction time, plasma power, oven or reactor heating temperature, the separation method, sampling method, and sampling collection time. [Display omitted] • The hydrogen production from various water types by DBD plasma is investigated. • The presence of ammonia enhanced the hydrogen production of water vapour. • The water contamination has a negative effect on the plasma water decomposition. • The new rate equation showed a good agreement with experimental results. [ABSTRACT FROM AUTHOR]

• A bibliometric analysis of the relevant literature on tunnel lighting was conducted. • The literature is sorted by energy saving, visual comfort, and low carbon. • The relationship between energy saving and low carbon is discussed. • The relationship between energy saving and comfort is also discussed. • The optimization methods of tunnel light environment are summarized. A reasonable and applicative tunnel light environment is important to ensure driving safety. This review aims to contribute to this growing area of research by exploring the tunnel lighting, which expands from safety to visual comfort, energy saving, and low carbon This paper employs bibliometric method to a visual analysis of the relevant literature and summarizes current research efforts and development directions. Firstly, the literature is visually presented by CiteSpace to elaborate the state-of-art with the dimensions of the timeline graph, the keyword co-occurrence graph, and the institutions & countries co-occurrence graph. Afterwards the literature is reviewed from three major perspectives: energy saving, visual comfort, and low carbon. Current research is mainly on luminaire arrangement, auxiliary lighting and other measures to reduce energy consumption. Visual comfort is mainly improved by optimizing the light environment's physical quantities, e.g. luminance, color temperature, color rendering etc. Current measures to achieve low carbon targets include green energy, green photoconductivity, green luminaires and green operation. Energy saving, visual comfort and low carbon are interrelated and complementary. However, a systematic understanding of how tunnel lighting contributes to carbon emission is still lacking. There is still room for development in tunnel lighting to achieve full intelligent control and the goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

This paper introduces an algorithmic method for the design and resistance prediction of hydrofoil vessels for fast ferry applications, using only design speed and mass as input parameters. A database of resistance estimates has been created and shared online. Furthermore, a simple model is introduced for the extension of the results to off-design speeds. The efficacy of the models is demonstrated through three case studies. Firstly, the importance of accounting for the power requirement during take-off is investigated, concluding that a simple assumption of constant speed yields accurate energy estimates for routes exceeding approximately six nautical miles. Case study number two entails a comparative analysis of hydrofoil vessel resistance to that of slender catamarans across a wide range of vessel sizes and speeds. The results reveal a potential for reducing the energy requirement of passenger fast ferries by 30% to 50% through the use of hydrofoils. The paper concludes with an investigation into the influence of surface roughness on hydrofoil resistance, indicating significant deteriorating effects even at moderate roughness levels. • An algorithmic hydrofoil design method. • A database of hydrofoil resistance estimates. • A comparison of the resistance of hydrofoils and slender catamarans. • A study of the importance of take-off energy. • An investigation into the importance of roughness. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

While the effects of climate change are already being felt in many parts of the world, societies are compelled to drastically rethink the way they produce and manage energy, to reduce the greenhouse-gases emissions associated to the sector. Urban areas are a critical piece of the puzzle to achieve the energy transition and reach the ambitious climate targets set out at global and European scales. Positive energy districts (PEDs), a new concept introduced in 2018 by the Strategic Energy Technology Plan, constitute a bottom-up response to this massive problematic and an opportunity to accelerate the transition towards low-carbon energy models in cities. However, their deployment is currently facing a set of diverse and complex challenges, mainly arising from their novelty and the lack of practical experience. In that context, the contribution of the present review of the literature is twofold. First, it seeks to provide a broad perspective of the advances of the research on PEDs with a special focus on applied studies and practical experiences. Then, it aims to identify substantial challenges that are currently slowing down PEDs effective operationalization, and study to what extent researchers and practitioners are currently approaching them. To that end, 135 papers are selected, categorized, and analysed according to their topics, locations, climates, districts, studied solutions and social concerns. As a result, the paper gives an overview of the main trends and necessities of PEDs research and practice and draws recommendations on how to adequately address identified challenges. [Display omitted] • Systematic review focusing on applied studies and practical experiences. • Theorical framework defining PEDs' main concepts, principles, and challenges. • Assessment of included literature performed using two levels of analysis. • Lack of studies including public space, non-technical solutions, and social issues. • Need for holistic, flexible, and efficient methodologies to support PEDs design. [ABSTRACT FROM AUTHOR]

The selection of optimal design and the most efficient operational parameters for energy devices constitute a priority task for sustainable development and increasing energy efficiency within the net-zero emissions strategy. This is particularly important in adsorption cooling and desalination systems with poor performance due to unfavourable heat transfer conditions in conventional packed beds of adsorption chillers (ACs). Therefore, looking for additional ways of performance improvement is still challenging, especially covering different design variants and operational strategies. The existing complex, time-consuming and costly analytical, numerical and experimental methods, usually focused on a specific design and operating parameters of conventional packed adsorption beds, cannot tackle these comprehensive problems. Since artificial intelligence (AI) based models are considered tools that sometimes may overcome the shortcomings of the programmed computing approach and the experimental procedures, the paper introduces automated machine learning (AutoML) as a general approach for the design and optimization study of adsorption cooling and desalination systems. The double-effect, i.e. specific cooling capacity (SCP) and specific daily water production (SDWP) of various adsorption chillers (ACs) operating in large-, pilot- and small-scale adsorption cooling and desalination systems, is considered in the study. The paper also presents a novel big data optimization procedure for selecting the best operating and design strategy in adsorption cooling and desalination technology. Finally, a new concept of fluidized bed-type application in adsorption chillers is proposed, which allows for enhancing the performance of ACs. The presented approach can be referred to as a complementary design technique in adsorption cooling and desalination systems, besides the existing complex analytical and time-consuming numerical methods and expensive experiments. • Double-effect desalination and cooling production in adsorption chillers (AC) are evaluated • The new approach allows considering various cooling and desalination adsorption systems. • The novel concept of fluidized bed application in adsorption systems is proposed. • The developed models constitute powerful tools for optimizing ACs systems' performance. [ABSTRACT FROM AUTHOR]

• This paper presents a comprehensive review of machine learning applications in maritime research, focusing on Automatic Identification System data. • It explores how machine learning enhances data quality and aids in maritime decision-making. • The review discusses challenges in applying machine learning to Automatic Identification System data and proposes future research directions. • Emphasizes the need for advanced machine learning models and the creation of benchmark Automatic Identification System datasets for improved analysis. Automatic Identification System (AIS) data holds immense research value in the maritime industry because of its massive scale and the ability to reveal the spatial–temporal variation patterns of vessels. Unfortunately, its potential has long been limited by traditional methodologies. The emergence of machine learning (ML) offers a promising avenue to unlock the full potential of AIS data. In recent years, there has been a growing interest among researchers in leveraging ML to analyze and utilize AIS data. This paper, therefore, provides a comprehensive review of ML applications using AIS data and offers valuable suggestions for future research, such as constructing benchmark AIS datasets, exploring more deep learning (DL) and deep reinforcement learning (DRL) applications on AIS-based studies, and developing large-scale ML models trained by AIS data. [ABSTRACT FROM AUTHOR]

In recent years, the severe environmental degradation and high levels of fossil fuel consumption linked to conventional ship energy systems have drawn attention to the advancement of alternative ship energy systems. Consequently, ship energy systems based on the use of an electrical microgrid are coming to the fore as an increasingly popular alternative solution. However, managing the energy flows within a shipboard microgrid can be highly challenging due to the multiple energy sources (including renewable energy sources) and types of loads involved. Additionally, the integration of an energy storage system has been identified as an effective solution for improving the reliability of shipboard power systems, pointing out the important role of energy storage systems in maritime microgrids and their potential to enhance the energy management process. As such, effective energy management strategies are necessary to optimize performance and ensure that the vessel's energy demands are met while minimizing fuel consumption and reducing cost and environmental impacts. This paper presents a comprehensive review of such strategies and methods recently presented in the literature associated with energy management in shipboard microgrids integrating energy storage systems and examine the different techniques that can be utilized to achieve optimal system performance. Moreover, the paper also sheds light on the future challenges and perspectives imposed by the modern shipboard microgrids operation on the energy/power management of these systems. The aim of this paper is to provide a helpful guideline for researchers and engineers conducting research in the field of electrically powered ships. [Display omitted] • A comprehensive review of EMS for electrical and hybrid ship is presented. • A comprehensive review of ESS which can be used in such ships is presented. • Technologies of ESS and their suitability to ships are discussed and commented. • Future challenges and perspectives imposed by modern SMG operation are presented. [ABSTRACT FROM AUTHOR]

The regenerator plays a crucial role in determining the energy efficiency of green liquid desiccant air-conditioning systems. This paper presents a novel air gap membrane distillation regenerator featuring heat recovery and portable water production. Based on a validated numerical model, performance evaluation of the regenerator combined with an external heat exchanger is conducted in terms of regeneration capacity (RC) and energy consumption (Q in). The comparison of flow patterns demonstrates that the counter-flow has a slightly higher RC , while the parallel-flow has a significantly lower Q in and is therefore preferable from the perspective of COP. The effects of key parameters including feed and coolant desiccant inlet temperatures (T f,in & T c,in), mass flow rate (m dil), membrane length (L), air gap thickness (δ ag), desiccant concentration (X dil) and solution heat exchanger effectiveness (ε she) on the performance are investigated. The results show that T f,in is the dominant factor on performance compared to T c,in and m dil , and increasing L leads to a marked growth in both RC and COP regardless of the raised Q in. Additionally, it is found that narrowing the air gap from 3 mm to 1 mm can improve RC and COP by 69.9 % and 50.1 % respectively. As for ε she , it has no influence on RC but remarkable impacts on Q in and COP. Moreover, the heat recovery by the coolant channel is proved to save nearly 8 % of energy input under the involved conditions. Finally, the NSGA-Ⅱ based multi-objective optimization is performed to obtain the Pareto front for maximizing RC and minimizing Q in. And the optimal solution through TOPSIS decision-making offers a RC = 975.8 g/h and a Q in = 1938 W. The findings provide a basis for design and application of the novel regenerator. • A novel air gap membrane distillation liquid desiccant regenerator is investigated. • Parallel-flow pattern is superior to counter-flow pattern in terms of energy efficiency. • Heat recovery by the regenerator can achieve about 8 % energy savings. • Effects of various design and operation parameters on the performance are explored. • Trade-off between regeneration capacity and energy consumption is realized by NSGA-Ⅱ. [ABSTRACT FROM AUTHOR]

Energy efficiency is a complicated process in designing Wireless Sensor Networks (WSNs). Effective routing is a precise solution for the energy efficiency constraint in WSN owing to the higher sharing of energy consumption during communication. Thus, routing is an alternative way out to this issue along with hierarchy-based clustering techniques. The clustering in WSN is carried out by picking the Cluster Head (CH), which is assumed as the essential or leader node. They perform more tasks, and thus, more energy is consumed. The clustering classifies the nodes into groups to avoid the huge communication range that is deputized to the CH. Hence, this paper establishes a routing algorithm for saving energy using a new optimization strategy via solving the multi-function formulation of the developed protocol in WSN. Here, the cluster head selection is performed with the Adaptive Remora Optimization Algorithm (AROA) to derive the multi-function derivation. The multi-function formulation undergoes with known factors such as energy consumption, distance, throughput, Packet Delivery Ratio (PDR), and path loss. The experimental result indicates an enhanced network lifetime and saving the energy of the developed heuristic-based routing protocol in WSN. [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]

• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]

This paper reviews the recent evolution of unmanned aerial vehicles (UAVs) through the lens of thermodynamic design. Emphasis is placed on using constructal theory in order to understand the relationship between different degrees of freedom within these complex dynamic systems. Size scale, the way in which UAVs are categorized based on weight and dimensions and one of the most significant factors in the design of any UAV, is analyzed in terms of its relationship to energy consumption. Specifically, constructal theory is used to predict the minimum size at which any UAV will need to be tethered in order to support its energy requirements. [ABSTRACT FROM AUTHOR]

• Thermal behavior of rectangular fins located within a square cavity were analyzed. • Inverse CFD method and experimental data were adopted for heat transfer prediction. • Suitable turbulence model based on the RMSE of the flow model were selected. • Effect of fin geometry on the flow and heat transfer within a cavity was evaluated. • Effect of PCM heat sink on the flow and heat transfer within a cavity was studied. This study examines the fluid flow and heat transfer characteristics of rectangular fins positioned within a square cavity through the use of the inverse three-dimensional computational fluid dynamics (CFD) method, experimental analysis, and the constant heat transfer rate assumption. This paper proposes a heat sink that uses a phase change material (PCM) made of paraffin within a small cavity. The inverse CFD method, combined with the least squares method, root mean square error, and excessive experimental data, is adopted to predict the unknown heat transfer rate Q and absorption heat Q ab. One of the objectives of this study is to investigate the effect of the PCM heat sink on fluid flow and heat transfer characteristics within a cavity. A sequence of tests on various flow models indicates that employing the RNG k-ε turbulence model with the standard wall function is the most suitable choice for all scenarios in the three-fin model. Conversely, the zero-equation model proved to be a more adequate fit for the PCM heat sink. Another objective of this study is to study the effect of the height of the PCM heat sink on Q and Q ab. The incorporation of a PCM heat sink results in a further improvement in the efficiency of heat dissipation. Part of the heat created is absorbed by the PCM heat sink, which absorbs 22.5 % of the thermal energy. Under the same volume, the PCM heat sink with a lower height (H p = 0.012 m) absorbs 14 % more heat compared to H p = 0.02 m. Thus, a lower-height PCM heat sink has a better heat dissipation effect. [ABSTRACT FROM AUTHOR]

The startup-shutdown behavior of the gas foil bearings-rotor system is vital for proton exchange membrane fuel cells due to the inherently frequent start-stop of hydrogen fuel cell vehicles, which significantly affects the sustainable power generation and efficiency of the fuel cells. In this paper, the effects of three systematic factors including bump foil structure, nominal clearance, and coating on the dynamic response of the bearings-rotor system are first investigated based on startup-shutdown experiments. The mathematical model considering both gas film and thermo-hydrodynamic characteristics is presented to investigate the startup-shutdown behavior further. The influencing mechanisms of the factors on performance are analyzed. The energetic results demonstrate that the bearing structure parameters have a significant influence on the startup-shutdown performance and energy consumption. Considering optimal startup-shutdown performance, a better bearings-rotor system is proposed. Compared to the average values in all cases, the take-off speed, temperature rise, and average energy consumption of the system are decreased by 51.3%, 16.2%, and 60.7%, respectively. This indicates that the startup-shutdown improvement of the bearing-rotor system has great significance for enhancing the energy efficiency of the fuel cell. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a decision making and motion planning system for lane change maneuvers considering energy efficiency. A novel decision making algorithm is proposed to check the desirability of performing the lane change. The algorithm is based on a utility function that consists of different performance criteria, including energy consumption. The execution of the decided maneuver involves a lower-level motion planning and control system for the longitudinal and lateral directions. For the longitudinal direction, an energy-efficient Model Predictive Controller (MPC) is designed, which considers the safety boundaries as well as other constraints, such as comfort, traffic laws, and physical limitations of the system. For the lateral direction, the desired trajectory is planned based on a parameterized sigmoid function. The lateral tracking is then realized by a PID controller. Finally, to evaluate the performance of the designed algorithms, a fuel consumption map of an internal combustion engine (ICE) is approximated by a second-order multivariate polynomial. Simulation results demonstrate the capability of the proposed algorithm to safely perform the lane change maneuver in different scenarios and for two vehicle models, including a simplified vehicle dynamic model and a high-fidelity IPG CarMaker model. [Display omitted] • A new lane change decision making is proposed considering energy consumption. • An integrated energy-efficient planning and control framework is designed using MPC. • The framework considers safety, comfort, energy consumption, and actuator limitation. • The fuel map of an internal combustion engine is fitted to second-order polynomial. • The obtained polynomial is used in the optimization problem of energy-efficient MPC. • Simulation study for a simple mathematical model and a high-fidelity CarMaker model. [ABSTRACT FROM AUTHOR]

Most hydraulic drive systems for robots are configured with one single pump and multiple cylinders, which suffers from low energy efficiency in the case of driving the variable loads. To solve the problem, this paper presents a multi-chamber hydraulic cylinder based on the bionic muscle drive mechanism. Firstly, the mechanical structure is designed, the mathematical models are derived, the mechanism of high energy efficiency is analyzed. Then, the simulation of load matching control is carried out, which shows the high energy efficiency improvement and the good position tracking. Finally, the multi-chamber hydraulic cylinder prototype is developed for experimental study, and output forces, output displacement, energy consumption, and friction characteristics are researched. The theoretical analysis and experimental research show that the bionic multi-chamber hydraulic cylinder can realize the power matching between the single power source and the variable loads in the hydraulic system, which provide a novel idea for the energy efficient hydraulic equipment. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze through simulation how the energy efficiency of a single electrolysis stack at various loads is affected by shunt currents and to determine the energy-optimal load distribution between lines in a multiline electrolysis system under various magnitudes of shunt current losses. A dynamic energy and mass balance model of an industrial 3 MW , 16 bar alkaline water electrolyzer (AWE) process was developed using MATLAB. The optimization goal is to determine the power supply for each AWE line so that it can meet any hydrogen demand while minimizing the global specific energy consumption (SEC). The Particle Swarm Optimization (PSO) algorithm is used to minimize the objective function. According to the results of the single stack investigation, shunt current reduction could significantly improve the energy efficiency of partial-load operation. In addition, the optimization study revealed that whenever two or more lines are required to run in order to satisfy the hydrogen demand, the global SEC is minimized when the lines operate at equal loads. [ABSTRACT FROM AUTHOR]

• An unequal grouping algorithm is introduced in this work to address the energy-hole problem in Internet-of-things (IoT) and wireless sensor networks (WSNs). • The work introduced an energy level-based group head selection model that considers multi-objective parameters such as hop count, energy, and packet loss ratio. • An energy and delay aware routing optimization mechanism is introduced to reduce delay and minimize energy consumption. • Finally, a multipath-based route is established, and effective load balancing is done for transmitting packets with QoS (quality of service) and without QoS. • The existing methods' performance is generally studied considering total node death; however, the EDAR model is studied considering different scenarios such as first node death and total node death. • The simulation results show the EDAR is very efficient in reducing communication overhead, delay, enhanced lifetime, and improved throughput in comparison with the recent state-of-art routing mechanisms such as low energy adaptive clustering hierarchy (LEACH), unequal clustering algorithm (UCA), and reliable and energy efficient routing (REER). The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead. Graphical abstract of energy and delay aware routing design. [Display omitted] [ABSTRACT FROM AUTHOR]

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration. [ABSTRACT FROM AUTHOR]

Lower limb exoskeletons have garnered significant attention for their effectiveness in gait training for paraplegic patients. For patients with insufficient trunk or upper limb strength to maintain balance, employing an unpowered robotic walker with an exoskeleton for gait training is an effective approach. The energy efficiency of the actuation system is a pivotal consideration in the design of the exoskeleton–walker system due to its significant influence on the system's durability and service efficiency. The main contribution of this paper is the development of a Parallel Compliant Leg (PCL) for the exoskeleton–walker system. The PCL consists of both powered legs of the exoskeleton and a passive flexible mechanism within the walker. This integration allows for the storage and release of energy during cyclic walking, resulting in reduced system energy consumption. To enhance energy efficiency, the support force optimization of the flexible mechanism is established. Based on this optimization, a design scheme and parameter optimization for the flexible mechanism are proposed. The effectiveness of the proposed flexible mechanism is verified through simulations on a robot simulation platform. Experimental results demonstrate a remarkable 67.6% reduction in system energy consumption achieved by the optimized mechanism. [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]

Aggressively reducing the supply voltage (V d d) below the safe threshold voltage (V m i n) can effectively lead to significant energy savings in digital circuits. However, operating at such low supply voltages poses challenges due to a high occurrence of permanent faults resulting from manufacturing process variations in current technology nodes. This work addresses the impact of permanent faults on the accuracy of a Convolutional Neural Network (CNN) inference accelerator using on-chip activation memories supplied at low V d d below V m i n. Based on a characterization study of fault patterns, this paper proposes two low-cost microarchitectural techniques, namely Flip-and-Patch, which maintain the original accuracy of CNN applications even in the presence of a high number of faults caused by operating at V d d < V m i n . Unlike existing techniques, Flip-and-Patch remains transparent to the programmer and does not rely on application characteristics, making it easily applicable to real CNN accelerators. Experimental results show that Flip-and-Patch ensures the original CNN accuracy with a minimal impact on system performance (less than 0.05% for every application), while achieving average energy savings of 10.5% and 46.6% in activation memories compared to a conventional accelerator operating at safe and nominal supply voltages, respectively. Compared to the state-of-the-art ThUnderVolt technique, which dynamically adjusts the supply voltage at run time and discarding any energy overhead for such an approach, the average energy savings are by 3.2%. [ABSTRACT FROM AUTHOR]

As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal "black box" of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG 6 E) and SDG7 efficiency (SDG 7 E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG 7 E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization. [Display omitted] [ABSTRACT FROM AUTHOR]

Electric vehicles have been considered into effective solutions to address energy problems in urban traffic systems for their remarkable performance in energy costs and carbon emissions reduction. However, the energy-saving effect of electric vehicles in urban traffic systems is restricted due to the complexities arising from mixed traffic conditions. To improve energy efficiency, this paper proposes an eco-driving-based mixed vehicular platoon control model for successive signalized intersections with connected and autonomous vehicles (CAVs), connected vehicles (CVs) and manned vehicles (MVs). Firstly, the dynamics model and spacing policy are analyzed, then the optimal platoon size and speed are calculated according to different traffic scenarios. Secondly, the mixed vehicular platoon control algorithm is established by considering traffic timing, energy consumption and string stability. Thirdly, an actual eco-driving-based platoon control system for tested electric vehicles is designed. The field test results demonstrate that all platoons can pass through intersections during green phase with minimum energy consumption, meanwhile the string stability can be guaranteed. It is noteworthy that the proposed model can greatly improve urban traffic capacity and energy efficiency. • A novel dynamical model of mixed vehicular platoon is explored by integrating different characteristics of CAV, CV, and MV. • An optimal speed algorithm of the leading vehicle to pass through successive signalized intersections is proposed according to signal timing, vehicular speed, and entry time. • The eco-driving-based mixed vehicular platoon control model is established, and the multi-objective function considering traffic environmental constraints are formulated to minimize the travel time and energy consumption. [ABSTRACT FROM AUTHOR]

The landscape of Demand-Side Energy Management (DSM) research is rapidly evolving, shaped by technological innovations and policy developments. This paper presents an exhaustive bibliometric analysis and methodological framework to explore the research trends within the DSM domain. By synthesizing data from Scopus and OpenAlex, we compile a comprehensive dataset of DSM publications that serve as the basis for our analysis. Through rigorous data acquisition and cleaning, we ensure the reliability and relevance of our dataset. We employ state-of-the-art Large Language Models (LLMs) and topic modeling techniques, including GPT and BERTopic, to perform semantic analysis and uncover thematic structures within the literature. Statistical analysis of the literature dataset reveals a steady increase in DSM publications, with significant contributions from prestigious journals and institutions worldwide. We observe that articles are the predominant publication type, while reviews often cite more references and receive higher citation counts. The distribution of publications over time indicates a growing interest in DSM, particularly since 2014. Geographical mapping of institutions highlights key regions contributing to DSM research, with notable outputs from Europe, North America, and East Asia. Coupled with citation network analysis, our approach reveals the influential works and emerging trends that define the scientific progression of DSM research. Our unsupervised topic modeling, powered by BERTopic, clusters the publications into distinct themes, while our advanced visualization techniques using UMAP and t-SNE provide insights into the semantic space of DSM literature. The resulting thematic classification is presented in a hierarchical structure, offering a comprehensive understanding of the field's focus areas. Our citation network analysis, utilizing force-directed graph computation and edge-bundling algorithms, maps the interconnectivity and impact of research contributions, providing a dynamic view of the field's evolution. This study not only charts the landscape of DSM research but also offers a methodological blueprint for future bibliometric analyses. The insights gained from this multi-faceted exploration serve as a valuable resource for researchers, policymakers, and industry practitioners looking to navigate the complexities of DSM and contribute to its scientific advancement. • Reviews energy demand-side management literature using bibliometric methods. • Uses citation network to reveal research clusters and interdisciplinary connections. • Uses GPT embedding and BERTopic model to cluster literature into ten themes. • Develops software tool for bibliometric analysis. [ABSTRACT FROM AUTHOR]

With the facilitation of agricultural modernization, China's agricultural energy consumption continues to rise. This paper empirically tests the agricultural energy rebound effect and its influencing factors in China during 1999–2020. It is believed that the improvement of energy efficiency has not led to the expected energy-saving effect in agriculture due to the "Jevons paradox" of energy efficiency and energy consumption. The average energy efficiency of China's agricultural energy consumption was 0.446, and the average energy rebound effect was 68.9 %. The level of agricultural mechanization, the effective irrigated area of farmland and agricultural investment have a positive impact on the rebound effect of agricultural energy, and the direct effects are 0.062, 0.008 and 0.064, respectively, while agricultural labor force and energy price have a negative impact on the rebound effect of agricultural energy. In particular, technology spillover and scale spillover led by agricultural technology progress have strong negative space spillover effect. As it is argued, 1 % of agricultural technology progress can reduce 3.5 % of agricultural energy rebound effect in surrounding areas. In order to reduce the energy rebound effect, it is suggested to increase the subsidies to agricultural labors and rationally formulate regional differentiated energy regulation policies. • The internal mechanism of rebound effect is expounded from the view of producers and consumers. • The average energy efficiency of China's agricultural energy consumption was 68.9 % during 1999–2020. • The determinants of the agricultural energy rebound effect were further discussed by considering the spatial spillover effect. [ABSTRACT FROM AUTHOR]

The extensive utilization of wireless services, coupled with the exponential surge in data traffic, has triggered substantial energy consumption. Thereby, growing attentions have been paid to energy-efficient heterogeneous cellular networks (HCNs). In this paper, we concentrate on the joint resource allocation issue under the framework of the non-orthogonal multiple access (NOMA) assisted HCNs with wireless backhauls. To furthermore improve the total system energy efficiency (EE) while ensuring the quality of service (QoS), we formulate the optimization problem as a hybrid joint resource allocation (HJRA) of integer programming and continuous optimization under the cross-tier interference threshold and minimum data rate constraint. The non-convex nature of the optimization problem arises from the fractional objective function and non-convex constraints, making it intractable to efficiently get an exact solution. In this regard, a quantum-inspired political optimizer (QPO) algorithm is devised to find the optimal solution for the HJRA issue, which realizes synchronous allocation of backhaul bandwidth, sub-channels, and power. Simulation results confirm the superiority of the QPO regarding the total system EE over other strategies. [ABSTRACT FROM AUTHOR]

This article analyzes energy poverty in Latin America and the Caribbean and the importance of energy efficiency to address this issue. The paper also touches upon energy subsidies in Mexico and their negative environmental and social consequences. Then, the article studies the potentially detrimental billing practices of Mexico's state electricity company, which are provoked by the enterprise's growing past-due portfolio and losses, and the possible infringement of electricity consumers' rights. The authors propose three courses of action for consumers to protest the company's possibly perjudicial billing. Finally, the authors suggest that a distributed electricity system in Mexico could combat energy poverty and strengthen electricity users' rights. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) have received a lot of interest because of their ability to cut fuel usage by using electricity from the grid as an alternative energy source. PHEVs differ from typical hybrid electric vehicles (HEVs) in terms of increased battery capacity, the availability of plug-in recharging, and improved battery state of charge (SOC) management systems. Efficient energy consumption and excellent performance are critical issues for hybrid powertrains. The estimation of SOC in PHEVs is a difficult NP-hard problem that is frequently tackled utilising metaheuristic techniques. In this paper, we offer an enhanced dingo optimisation with deep learning-based prediction (EDOA-DLP) model specifically tailored for PHEVs. The EDOA-DLP model includes an efficient SOC estimate algorithm for real-time energy management. The EDOA-DLP technique's performance has been thoroughly proven through considerable experimentation in a variety of circumstances. The results show that the proposed EDOA-DLP method beats other current methods in terms of predicting SOC accurately and reaching the targeted goals for PHEVs. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results highlight the EDOA-DLP model's improved performance. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results illustrate the EDOA-DLP model's improved performance when compared to competing methodologies, highlighting its potential to improve the efficiency and efficacy of PHEV systems. [ABSTRACT FROM AUTHOR]

Intermittent embedded devices and systems are widely used in various scenarios, but they often experience power failures due to unstable power supplies. Non-volatile memory (NVM) is gaining popularity in embedded systems due to its byte-addressability, low access latency, and high density. As a result, backups can be stored in NVM, and global data are directly manipulated in NVM in this architecture. However, NVM has limited write endurance and modifications to global data frequently occur which results in a significant number of writes. As a result, the extensive NVM accesses may lead to poor performance for intermittent programs. This paper presents an energy-efficient approach, called shadow-stack to minimize NVM write overhead while ensuring consistency. Furthermore, we propose a series of principles to assist programmers in rewriting the programs. And we prove the correctness of these principles using formal methods. Experimental results demonstrate that shadow-stack achieves an approximately 95.1%, 81.3%, and 76.6% improvement in NVM writes overhead compared with log-based, Coach, and Clank, respectively on average. Simultaneously, shadow-stack also significantly reduces energy and time consumption compared with log-based approach by up to 19.4 × and 6.87 × , Coach by up to 4.2 × and 2.46 × , and Clank by up to 3.4 × and 2.26 × respectively. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis functions as a necessary tool to prevent substantial damage to industrial products and enhance system reliability. While Artificial Neural Networks (ANNs) have been extensively studied in this context, they still face substantial challenges in resource consumption, robustness, and generalizability. To overcome these limitations, researchers have developed the third-generation neural network based on brain's structure, namely the Spiking Neural Network (SNN), which leverages the concept of time steps and spiking signals for enhanced spatiotemporal feature processing and energy efficiency. This paper proposes the Wide Spiking Residual Grouping Attention Framework (WSRGA-FW), which incorporates the advantages of both ANN and SNN. The WSRGA-FW employs Extended Gramian Representation for signal encoding to reduce noise impact, followed by a tailored ANN with wide convolutional kernels, optimized residual structures, and Grouped Perception Generation (GPG) Layers. These augmentations increase the network's representation and robustness, particularly in noisy environments. The backbone ANN is transformed into an SNN model, allowing deployment in portable and miniaturized devices with improved application prospects. Performance evaluations across various noisy scenarios using bearing fault datasets demonstrate that WSRGA-FW surpasses existing networks. Visualization of firing rates and energy consumption calculation contribute to the interpretability and intrinsic energy efficiency advantages of SNNs. [ABSTRACT FROM AUTHOR]

• Improves energy efficiency in SDN networks. • Effectively balances network load with meta-heuristic algorithms. • Dynamically optimizes resource allocation. • Ensures efficient utilization of network resources. • Minimizes energy consumption. Software-Defined Networks (SDN) represent an efficient approach for managing large-scale networks, aiming to enhance resource use, performance, cost efficiency, and quality of service for dynamic services. By segregating the control plane and utilizing a centralized software controller, SDN offers notable capabilities in software development, programmability, and flexibility. Nevertheless, balancing energy efficiency and processing power remains a key challenge. This paper introduces an interactive scheme within the SDN controller, employing the krill herd meta-heuristic algorithm for optimization-based programming. The proposed method emphasizes the interaction between load balancing (LB) and energy efficiency, leveraging control plane data and virtual machine (VM) updates to derive optimal solutions. Simulation results illustrate an average 13.5 % enhancement in SDN load balancing, a 25.9 % improvement in energy efficiency, and an approximate 20 % performance boost, showcasing the effectiveness of the proposed approach. [Display omitted] [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]

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]

This paper delves into the potential impact of a changing climate on the energy performance of European buildings. Research aims to provide a comprehensive evaluation of current energy requirements focusing on the envelope, considering existing regulations in national policies. Energy simulations are conducted at 94 locations across the European Union to cover the climatic variability and Koppen climate classification. The research analyzes future climate scenarios for the years 2030, 2050, and 2070, using three different Representative Concentration Pathways (RCP 2.6, 4.5, 8.5). According to a comprehensive analysis of heating, cooling, and overall energy performance, climate plays a significant role in buildings' energy balance. In moderately cool climate countries, the demand for air conditioning is projected to decrease in the years ahead. Conversely, in countries with a warm climate, there is a projected increase in the overall energy demand. Consequently, a revision of current energy regulations should be a priority. Providing insights into the relation between building design, energy efficiency, and climate change, the research identifies policy adjustments to ensure buildings can effectively respond to changing climatic conditions. A holistic and dynamic approach can support building design accounting for long-term impacts of climate change to create resilient and energy-efficient structures. • National building energy regulations of European Member States do not tackle climate change. • Over next decades, a decrease in heating demand is forecasted across all climate zones. • There is potential for a future considerable increase of cooling demand in Europe. • A reduction in total building energy demand in colder climates and an increase in warmer climates can be expected. • Revise national energy regulations, especially in warmer zones, to curb the expected rise in Europe's total energy demand. [ABSTRACT FROM AUTHOR]

• PV power generation coupling with hydrogen production system is modeled. • Techno-economic performance of the system for five cities in China is investigated. • The system efficiency and LCOH range from 9.03 to 9.31% and 22.54 to 24.97 CNY/kg. In recent years, photovoltaic power generation technology has developed rapidly, but due to its impact on the stability and security of the power grid, some areas in North and Northwest China have a certain degree of photovoltaic power curtailment phenomenon. Hydrogen is considered a good medium for energy storage, and the photovoltaic power generation system based on hydrogen energy storage has been the focus of research. Therefore, this work established simulation models of a photovoltaic power generation with a rated capacity of 100 MW coupling with hydrogen production system using MATLAB software and performs real-time simulation. Techno-economic performance of the integration system in five cities of Hami, Ordos, Datong, Yinchuan, and Yulin was analyzed. The results show that the coupling system can generate 158484–175675 MWh of electricity annually, which can drive the electrolyzer to produce 2524–2761 tonnes of hydrogen. The energy efficiency of the overall system and levelized cost of hydrogen are 9.03–9.31 % and 25.95/22.54–28.38/24.97 CNY/kg. The total CO 2 emissions reduction and carbon credits earned by the established system over a lifetime of 20 years can reach 1.81–2.00 million tonnes and 92.19–102.19 million CNY. This paper will provide economic and energy efficiency reference for studying the coupling of green hydrogen production systems with the coal chemical industry in the above regions. [ABSTRACT FROM AUTHOR]

Approximate computing is one of the promising techniques in error-resilient applications to overcome high-density integration challenges, such as energy consumption and performance. Multipliers constitute a significant portion of computer arithmetic units, leading to considerable energy and time consumption. In this paper, we propose low-power and compact approximate compressors for composing approximate Dadda multiplier structures, including compressors, half adders, and full adders, which utilize three-phase partial product compression: truncated, approximation, and exact columns. In approximate columns, we consider approximate compressors derived from the truth table of the exact 4:2 compressor and simplified K-map entries based on the probability of each combination of inputs. An error-correcting module (ECM) is designed to distinguish specific cases and reduce the error metrics. All circuits were simulated using ModelSim and then synthesized using Design Compiler with the 15 nm FinFET technology. When compared to state-of-the-art works, our multipliers exhibit approximately 30%, 43%, and 10% reductions in power, area, and delay, respectively. To evaluate their functionality, we conducted image multiplication and implemented a simple multi-layer perceptron (MLP) neural network using the modified National Institute of Standards and Technology (MNIST) dataset in MATLAB with 0.998 mean structural similarity index metric (MSSIM), 51 dB peak-signal noise ratio (PSNR), and 95% classification accuracy. • Introducing novel ultra-compact approximate compressor structures to reduce design metrics. • Proposing an error-correcting module (ECM) for compensating errors of approximation. • Proposing truncated 8 × 8 Dadda multipliers using the proposed approximate compressors and ECM. • Evaluating the accuracy of approximate multipliers in error-tolerant applications. [ABSTRACT FROM AUTHOR]

Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements. [ABSTRACT FROM AUTHOR]

The startup performance is crucial to the high-efficient operation of the sustainable power generation system. Current literature usually focuses on catalysts and startup strategy to improve startup performance of the proton exchange membrane fuel cell, which ignores the gas foil bearings-rotor system that directly affects startup response. Therefore, in this paper, the effects of bump foil structure, coating, and nominal clearance on the startup response of the bearings-rotor system are first investigated experimentally. Multi-objectives optimization mathematical model considering simultaneously lower power consumption, takeoff speed, and vibration is presented based on grey relational analysis. The optimal bearings-rotor system with radial trisection bump foil, coating MoS 2 , and nominal clearance of 50 μm is identified. Compared to average values, the power consumption of the optimal system is diminished by 27.2% while the takeoff speed and nonlinear vibration are reduced by 16.2% and 47.3%, respectively. These findings can be used to improve energy efficiency and startup performance for fuel cells. The systematic methodology proposed in this study can be extended to other investigations about the startup. • Bump foil type, coating, and nominal clearance are first considered in PEMFCs. • Multi-objective optimization mathematical model is presented. • Optimal gas foil bearings-rotor system is proposed and verified. • Startup performance of the proposed system is improved. • Power consumption of the proposed system is diminished by 27.2%. [ABSTRACT FROM AUTHOR]

Over the years, numerous studies have explored the green synthesis of ethylene. Within this context, the focus of this perspective shifts toward plasma technology, which has demonstrated the capability to convert methane into ethylene. Plasma catalysis creates distinctive physical and chemical environments, particularly at normal temperature and pressure, distinguishing it from alternative methods. Nevertheless, the utilization of atmospheric pressure plasma is intricate, posing scientific challenges in the realms of physics and chemistry. In this viewpoint, various key performance aspects are evaluated, encompassing methane conversion efficiency, ethylene selectivity, and specific energy input. These scientific pros and cons are then assessed for their readiness for industrial-scale implementation. Initially, the potential for small-scale ethylene production is examined, leveraging existing robust process technologies to unlock fresh market and supply chain opportunities. Subsequently, the sustainability of plasma technology for green ethylene production is compared to conventional ethylene production and alternative green ethylene production methods, including biomass-based approaches. Contrary to perhaps optimistic expectations, current literature evidence does not uniformly favor the latter, indicating the potential for plasma-based green ethylene processes. Additionally, this paper underscores the importance of considering Environmental, Social, and Governance factors that influence business decisions. Finally, this review underscores plasma technology as a potentially promising approach for green ethylene synthesis from methane, offering unique advantages under normal conditions while simultaneously presenting scientific challenges. It assesses its viability for small-scale production and benchmarks its sustainability against conventional and alternative methods, emphasizing the importance of a sustainable future for the green petrochemical industry. • Potentials of plasma catalytic methane conversion are compared with other green methods. • Conversion efficiency, selectivity, and sustainability are reviewed in terms of specific energy input. • Small-scale plasma technology enables robust ethylene production and opens new market opportunities for green synthesis. • Green ethylene is analyzed in relation to green hydrogen and ammonia from different aspects. • Plasma-based green ethylene processes are more sustainable than conventional and biomass-based methods. [ABSTRACT FROM AUTHOR]