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Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033. [ABSTRACT FROM AUTHOR]

In the past, hydrogen was mostly produced from fossil fuels, causing a certain degree of energy and environmental problems. With the development of low-carbon energy systems, renewable energy hydrogen production technology has developed rapidly and become one of the focuses of research in recent years. However, the existing work is still limited by small-scale hydrogen production systems, and there is a lack of comprehensive research on the whole production-storage-transportation-utilization hydrogen system (PSTUH2S), especially on the modeling of different hydrogen transportation modes and various hydrogen loads in different fields. To make up for these deficiencies, the specific physical and mathematical models of the PSTUH2S are firstly described in this paper, with a full account of large-scale water-electrolytic hydrogen production from renewable power curtailment and grid power, various hydrogen storage and transportation modes, and multi-field hydrogen consumption paths. Furthermore, to achieve the maximum economic, energy, and environmental benefits from the PSTUH2S, a multi-objective nonlinear optimization model is also presented herein and then solved by the hybrid method combining the nonlinear processing method, the CPLEX solver and the piecewise time series production simulation method. Lastly, case studies are conducted against the background of a region in northwest China, where hydrogen consumption capacity in various years is accurately assessed and the potential advantages of the PSTUH2S are demonstrated. As the simulation results show, the power curtailment of renewable energy generation can be reduced by 3.61/11.87/14.72 billion kW·h in 2025/2030/2035, respectively, thus contributing to a 4.98%~10.09% increase in the renewable energy consumption rate and millions of tons of carbon emission reduction in these years. In terms of the total equivalent economic benefits, the proposed method is able to bring about a cost saving of USD 190.44 million, USD 634.66 million, and USD 865.87 million for 2025, 2030, and 2035, respectively. [ABSTRACT FROM AUTHOR]

This paper presents the energy, exergy, and economic analysis of a new cogeneration cycle for the simultaneous production of power and cooling operating with an ammonia–water mixture. The proposed system consists of an absorption cooling system integrating a reheater, a separation tank, a compressor, a turbine, and an expansion valve. In addition, internal rectification is applied, improving the system's performance. Mass, energy, and exergy balances were applied to each system's component to evaluate its performance. Additionally, the costs of each component were determined based on economic equations, which take into account mass, heat flows, and temperature differences. A parametric analysis found that the system reached an energy utilization factor of 0.58 and an exergy efficiency of 0.26 using internal rectification at TG = 120 °C, TA = 30 °C, and TE = 10 °C. The power produced by the turbine was 26.28 kW, and the cooling load was 366.8 kW. The output costs were estimated at 0.071 $/kW. The condenser was found to be the most expensive component of the system, contributing 28% of the total cost. On the other hand, it was observed that the generator was the component with the highest exergy destruction, with 38.16 kW. [ABSTRACT FROM AUTHOR]

To improve the efficiency of the multidisciplinary design optimization of underwater vehicles, this paper proposes a combined proxy model with adaptive dynamic sampling. The radial basis function model (RBF), Kriging model, and polynomial response surface model (PRS) are used to construct the proxy model. Efficient sample points are collected based on the synthetic minority oversampling technique (SMOTE) algorithm and the lower confidence bound (LCB) criterion. The proxy model process is integrated after dynamic sampling. The collaborative optimization framework is used, which considers the coupling between the main system set and the subsystem set. The hierarchical analysis method is used to transform the multidisciplinary optimization problem into a single-objective optimization problem. Computational fluid dynamics (CFD) numerical simulation is utilized to simulate underwater submarine navigation. The optimization strategy is applied to the underwater vehicle SUBOFF to optimize resistance and energy consumption. Three dynamic proxy models and three static proxy models are compared. The results show that the optimization efficiency of the underwater vehicle has been improved. To prove the generalization performance of the proposed combined proxy model, a reducer example is investigated for comparison. The results show that the combined proxy model (CPM) is highly accurate and has excellent generalization performance. [ABSTRACT FROM AUTHOR]

In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency. [ABSTRACT FROM AUTHOR]

With the continuous development of the power Internet of Things (PIoT), smart devices (SDs) have been widely used in electric power inspections. Due to the limited resources of intelligent inspection SDs and the distance of overhead transmission lines, many inspection tasks cannot be processed promptly. This paper proposes a multi-hop-based end-edge cooperative computing (MHCC) scheme to address inspection task processing in power IoT. We formulate a multi-hop task offloading problem that minimizes the energy consumption of inspection SDs with delay constraints. We develop a JDPSO algorithm to solve the multi-hop task offloading problem and evaluate the algorithm's performance based on numerous simulation experiments. The experimental results show that JDPSO reduces the system's energy consumption by 56.30%, 48.56%, 78.87%, 50.89%, 30.85%, and 68.31%, and also reduces delay by 50.69%, 42.78%, 58.67%, 44.84%, 10.22%, and 62.88% compared to GA, RSA, SSA, MFO, DOA, and ALC schemes. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent stochastic model is recommended for the optimization of a hybrid system that encompasses wind energy sources, battery storage, combined heat and power generation, and thermal energy storage (Wind/Battery/CHP/TES), with the inclusion of electric and thermal storages through the cloud theory model. The framework aims to minimize the costs of planning, such as construction, maintenance, operation, and environmental pollution costs, to determine the best configuration of the resources and storage units to ensure efficient electricity and heat supply simultaneously. A novel meta-heuristic optimization algorithm named improved horse herd optimizer (IHHO) is applied to find the decision variables. Rosenbrock's direct rotational technique is applied to the conventional horse herd optimizer (HHO) to improve the algorithm's performance against premature convergence in the optimization due to the complexity of the problem, and its capability is evaluated with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) methods. Also, the cloud theory-based stochastic model is recommended for solving problems with uncertainties of system generation and demand. The obtained results are evaluated in three simulation scenarios including (1) Wind/Battery, (2) Wind/Battery/CHP, and (3) Wind/Battery/CHP/TES systems to implement the proposed methodology and evaluate its effectiveness. The results show that scenario 3 is the best configuration to meet electrical and thermal loads, with the lowest planning cost (12.98% less than scenario 1). Also, the superiority of the IHHO is proven with more accurate answers and higher convergence rates in contrast to the conventional HHO, PSO, and MRFO. Moreover, the results show that when considering the cloud theory-based stochastic model, the costs of annual planning are increased for scenarios 1 to 3 by 4.00%, 4.20%, and 3.96%, respectively, compared to the deterministic model. [ABSTRACT FROM AUTHOR]

In recent advancements, the traditional von Neumann architecture has been challenged by the computational needs of AI. This is due to its high power and data transfer costs. As a solution, the computing-in-memory (CIM) architecture, which combines storage and computation, has gained attention for its superior computational power and energy efficiency. Within CIM, using resistive random access memory (RRAM) arrays, the readout circuit, which converts analog outputs from multiply–accumulate operations into digital signals, faces limitations due to its area and power consumption. There are mainly two types of CIM readout circuits for analog types: the traditional ADC type and the non-traditional type. This paper presents two types of readout circuit designs. The first is a low-power, compact successive approximation register (SAR) analog-to-digital converter (ADC) readout circuit. The core circuit is an 8-bit SAR ADC operating at 70 MS/s. It incorporates a linearity-improved bootstrapped switch to minimize leakage and enhance linearity, whose spurious-free dynamic range (SFDR) has been improved by 10.1 dB from 76.78 dB to 86.88 dB, and whose signal-to-noise and distortion ratio (SNDR) has increased by 4.56 dB from 75.13 dB to 79.69 dB. The delay of a transconductance-enhanced dynamic comparator is reduced from 184 ps to 149 ps, presenting a performance improvement of approximately 20%. Concurrently, the energy consumption decreased from 178 μm to 132 μm, attaining an improvement of roughly 26%. A "sandwich" capacitor structure is used that reduces the overall area of the layout. After layout and post-simulation, this circuit occupies only 49.6 μm × 51.5 μm, consumes 553 μW power, has a SINAD of 46.22 dB, and has an SFDR of 57.21 dB. The second is a current controlled oscillator (CCO)-type readout circuit, which comprises a CCO oscillator with low process-sensitivity. The readout circuit also utilizes an op-amp and current mirrors for a negative feedback loop, ensuring a constant voltage across the RRAM arrays. The frequency generated through the CCO is controlled by the current, and quantified by a counter, supporting different weights quantification per ReRAM column without additional digital weighting. This circuit achieves 95-level resolution, 5.2 μs delay, and an average consumption of 183.1 μW. A comparative analysis highlights that traditional ADC readout circuits offer high resolution and speed but are limited by their high power and area costs, often overshadowing CIM arrays' benefits. Thus, for applications with more lenient resolution and speed requirements, non-traditional readout circuits present considerable advantages. [ABSTRACT FROM AUTHOR]

Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition. [ABSTRACT FROM AUTHOR]

This paper reviews the fundamentals of the thermoeconomic diagnosis theory. Thermoeconomic diagnosis is one of the main applications of the exergy cost theory used to identify the causes of additional resource consumption of a system due to inefficiencies in its components, published in the late 1990s. Thermoeconomic diagnosis has usually been applied to diagnose power plants with high consumption of fossil fuels and fixed production. However, it does not consider the final production and waste generation variation. In this paper, Circular Thermoeconomics is applied to analyze in depth the effect of malfunctions on additional waste generation and changes in the final output of the system. This new formulation can be applied to polygeneration systems, where there is a simultaneous variation of final products, and to process integration and industrial symbiosis, where a part of the waste generated by a plant could be reused in other processes or plants. [ABSTRACT FROM AUTHOR]

The energy transition to a decarbonized energy scenario leads toward distributed energy resources in which end users can both generate and consume renewable electricity. As a result, several challenges arise in terms of decentralized energy resource management and grid reliability. With microgrids, the cooperation of distributed energy resources is improved, and with peer-to-peer energy exchange and demand response programs, better energy allocation and flexible management of consumption loads according to the needs of supply systems are achieved. However, effective peer-to-peer energy allocation and flexible demand management in microgrids require the development of market structures, pricing mechanisms, and demand response strategies enabled by a reliable communication system. In this field, blockchain offers a decentralized communication tool for energy transactions that can provide transparency, security, and immutability. Therefore, this paper provides a comprehensive review of key factors for peer-to-peer energy trading and flexible energy demand management in blockchain-enabled microgrids. The goal is to provide guidelines on the basic components that are useful in ensuring efficient operation of microgrids. Finally, using a holistic view of technology adoption as a tool for peer-to-peer communication in microgrids, this paper reviews projects aimed at implementing blockchain in energy trading and flexible demand management. [ABSTRACT FROM AUTHOR]

The paper presented studies on reducing traction energy consumption with a decrease in the weight of an all-metal gondola car. Based on the proposed mathematical criterion, a new form of a blind floor was obtained, which makes it possible to reduce the weight of an all-metal gondola car. The aim of the paper was to reduce traction energy consumption with a decrease in the weight of an all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, strength studies were performed based on the finite element method. The equivalent stresses of the blind floor of an all-metal gondola car were 140.6 MPa, and the equivalent strains were 7.08 × 10−4. The margin of safety of the blind floor of an all-metal gondola car was 1.57. The weight of an all-metal gondola car with a modified form of a blind floor was reduced by 5.1% compared to a typical all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, a comparison was made of the traction energy consumption with typical all-metal gondola cars. Traction energy consumption with empty all-metal gondola cars were reduced by 2.5–3.1%; with loaded all-metal gondola cars by 2.4–7.3%, depending on the travel time interval. [ABSTRACT FROM AUTHOR]

Energy poverty affects numerous households across the globe and has several key implications and concerns for public health and social equity. Energy poverty is defined as "the lack of access to modern and affordable energy services". Individuals or communities in energy poverty face limitations in accessing reliable, affordable, and sustainable energy. This review paper examines a focused subset of recent research on energy poverty highlighted by the "NSF 2026: Priorities and Research Needs for an Equitable Energy Transition" workshop and the United States Department of Energy's Office of Energy Justice Policy and Analysis to help frame energy poverty's impacts on policy, poverty alleviation, environmental impact, and social inequity. This review paper uses five themes to organize previous energy poverty work: (1) Energy Poverty and Justice Definitions and Metrics; (2) Behavioral Aspects of Energy Poverty; (3) Efficacy of Energy Assistance Programs; (4) Efficiency of Energy Efficiency Policy; (5) The Energy Transition and Environmental and Energy Justice. We found that the literature examined how comprehensive assessment of energy poverty requires going beyond standard statistics and metrics and must include an understanding of how underserved households interact with energy. We found strong optimism for the clean energy transition's ability to significantly alleviate energy poverty, but only if policymakers include equity. Finally, we found that while there is plenty of work highlighting deficiencies there is a dearth of work examining successful implementations and how to replicate them which will be needed if the clean energy transition is to match its potential. [ABSTRACT FROM AUTHOR]

Since their inception with Bitcoin in the late 2000s, cryptocurrencies have grown exponentially, reshaping traditional financial paradigms. This transformative journey, while innovative, brings forth pressing concerns about their energy consumption and carbon footprint. While many studies tend to zoom in on Bitcoin, this paper broadens the perspective by evaluating energy consumption across various cryptocurrencies. We analyze nine cryptocurrency projects, chosen for their market value, technology, and data availability. These span a spectrum from pioneering to emerging digital coins, offering a holistic view of the crypto realm. To contextualize, we juxtapose the energy usage of these digital currencies with traditional payment means like Visa and Mastercard. Our analysis shows vast differences in energy use among cryptocurrencies, largely tied to their consensus algorithms. Notably, while Bitcoin stands out as highly energy-intensive, several newer digital currencies have energy footprints mirroring those of conventional payment methods. Additionally, CO2 emissions estimation presents challenges due to variances in miner locations and regional energy sources, with potential higher emissions if concentrated in carbon-intensive regions like China. Nonetheless, the silver lining emerges as many cryptocurrencies, especially those beyond Bitcoin, register considerably lower CO2 emissions. By moving the lens beyond Bitcoin, this paper paints a more nuanced picture of the environmental ramifications of the crypto world. [ABSTRACT FROM AUTHOR]

Microgrid technology offers a new practical approach to harnessing the benefits of distributed energy resources in grid-connected and island environments. There are several significant advantages associated with this technology, including cost-effectiveness, reliability, safety, and improved energy efficiency. However, the adoption of renewable energy generation and electric vehicles in modern microgrids has led to issues related to stability, energy management, and protection. This paper aims to discuss and analyze the latest techniques developed to address these issues, with an emphasis on microgrid stability and energy management schemes based on both traditional and distinct approaches. A comprehensive analysis of various schemes, potential issues, and challenges is conducted, along with an identification of research gaps and suggestions for future microgrid development. This paper provides an overview of the current state of the field and proposes potential areas of future research. [ABSTRACT FROM AUTHOR]

In light of the pressing concerns about worldwide warming and environmental degradation, understanding the nexus between energy consumption and green development has become vital to fostering a low-carbon transition in energy consumption, and promoting environmentally friendly development. After exploring the connotations of energy consumption and green development, this paper constructed evaluation systems for energy consumption and green development. By leveraging quantitative methods; such as the entropy method, coupling coordination model, spatial Markov model, and gray model GM (1, 1); we conducted an empirical study into the dynamism and evolutionary trends in the coupling coordination degree between energy consumption and green development in China, spanning from 2006 to 2020. Our findings delineate several key trends: (1) overall, the levels of each system have witnessed a marked increase, with the average energy consumption slightly exceeding that of green development; (2) the coupling coordination degree has displayed a consistent rise over time, with spatial distribution patterns exhibiting a "higher in the south, lower in the north" and a "center-edge" characteristic; (3) the dynamic evolution of coupling coordination types manifests a stability, continuity, and heterogeneity, eliciting distinct effects across different neighbourhood types; (4) within the forecast period, the coupling coordination degree among Chinese provinces is projected to undergo further enhancement, with the majority of provinces transitioning from a barely coordinated stage to a coordinated development stage. Above all, to stimulate a more qualitative coupling coordination between energy consumption and green development, this paper provides relevant policy implications. [ABSTRACT FROM AUTHOR]

As an important means of farmland policy, whether land transfer can promote agricultural green transformation is worthy of further study; however, related research is relatively rare. Based on the inter-provincial panel data from 2005 to 2020, this paper examines the influence of land transfer on agricultural green transformation and its underlying mechanism by using a two-way fixed effect model and an intermediary effect model. This study reveals significant findings as follows: (1) Land transfer substantially promotes agricultural green transformation. (2) Energy consumption is a major contributor to the growth of agricultural carbon emissions; however, land transfer can mitigate this by reducing energy consumption. (3) Land transfer can promote agricultural green transformation by fostering agricultural technology progress. (4) Further analysis reveals that land transfer in economically developed areas and the southeastern side of the "Hu-Huanyong Line" significantly enhances agricultural green transformation. Based on these findings, this paper suggests promoting land transfer while considering regional differences. Additionally, attention should be directed towards reducing energy consumption and encouraging agricultural technology's progress. [ABSTRACT FROM AUTHOR]

Global energy consumption has led to concerns about potential supply problems, energy consumption and growing environmental impacts. This paper comprehensively provides a detailed assessment of current studies on the subject of building integrated photovoltaic (BIPV) technology in net-zero energy buildings (NZEBs). The review is validated through various case studies, which highlight the significance of factors such as building surface area to volume ratio (A/V), window-wall ratio (WWR), glass solar heating gain coefficient (SHGC), and others in achieving the NZEBs standards. In addition, this review article draws the following conclusions: (1) NZEBs use renewable energy to achieve energy efficiency and carbon neutrality. (2) NZEBs implementation, however, has some limitations, including the negligence of indoor conditions in the analysis, household thermal comfort, and the absence of an energy supply and demand monitoring system. (3) Most researchers advise supplementing facade and window BIPV as solely roofing BIPV will not be able to meet the building's electricity usage. (4) Combining BIPV with building integrated solar thermal (BIST), considering esthetics and geometry, enhances outcomes and helps meet NZEB criteria. (5) BIPV designs should follow standards and learn from successful cases. However, to ascertain the long-term reliability and structural integrity of BIPV systems, a comprehensive study of their potential degradation mechanisms over extended periods is imperative. The review paper aims to examine BIPV applications in-depth, underscoring its pivotal role in attaining a net-zero energy benchmark. [ABSTRACT FROM AUTHOR]

There is a height drop in the rain area of the circulating cooling water in mechanical ventilation circulating cooling towers, resulting in the ineffective use of gravitational potential energy. High-level water collection is an effective way to reduce the energy consumption of the cooling tower. Based on this, aiming to solve the gravity energy waste problem of circulating water in the cooling tower of a steel plant, this paper innovatively puts forward the high-level water tank to utilize the energy-saving transformation technology of turbine power generation and pump power saving. Additionally, this paper explores the energy-saving effects of the two methods under different height drops. The results show that the maximum utilizable rain area height of the cooling tower is 5 m, while the annual electric energy output of turbine technology can reach 4.704 million kW·h. The high water collection technology can reduce pump power consumption and save up to 7.35 million kW·h per year of electric energy, maintaining a more significant energy-saving ability compared with the turbine power generation technology. In terms of performance, the design of a high-level water tank is to help eliminate rain areas and improve the heat exchange efficiency of water and gas, so that the water temperature of the outgoing tower is 0.13 °C lower than that of the conventional cooling tower. Meanwhile, the ventilation resistance in the rain area is weakened, the resistance coefficient can be reduced by about 40–50%, and the noise can be reduced by about 10 dB (A) under the action of the water collection device. According to the economic evaluation, the total cost of turbine power generation technology is 0.563 million dollars and the total cost of high-level water collection technology is 0.446 million dollars. The cost can be realized within two years, but the high-level water collection technology avoids additional pump maintenance costs and has better economy. This study provides a theoretical basis for the transformation and optimization design of mechanical ventilation cooling towers, and has important reference value. [ABSTRACT FROM AUTHOR]

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles' power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks. [ABSTRACT FROM AUTHOR]

Fossil energy consumption and environmental protection issues have pushed electric vehicles (EVs) to become one of the alternatives to traditional fossil-fuel vehicles. EV refers to a vehicle that uses electric energy as power and is driven by an electric motor. The electric energy of EVs is stored in batteries. When the EV is not traveling, the battery can provide power for other loads. Therefore, with the increase in the number of EVs and the load of the power grid, the EV-to-grid (V2G) mode, which uses EVs to supply power to the power grid, has gradually entered the field of vision of researchers. The physical connection mode, charge and discharge technology, and energy management strategy are the main topics of the current review papers; however, there is a lack of systematic research on V2G modeling, framework, and business models. This paper describes the concepts of the spatio-temporal distribution model and the adjustable capacity of EVs. In addition, common constraints and methods in optimization are introduced. Moreover, this paper introduces the interactive relationship among power grids, load aggregators, and EV users. Furthermore, the business model of V2G is introduced and analyzed from various perspectives. Finally, the future development of V2G is pointed out. This paper's goal is to provide an overview of the present V2G application scenarios and to identify any challenges that must be overcome. [ABSTRACT FROM AUTHOR]

This paper provides recommendations for improved analyses of the performance of ground-coupled heat pumps. Most research on ground-coupled heat pumps focuses on improving the performance of borehole heat exchangers (BHE) and reducing system costs. However, the potential improvements are mainly assessed at the BHE level rather than considering the entire system incorporating a heat pump, circulation pump, and building needs. This paper shows that such an approach can be misleading, and improvements in BHE are significantly overestimated if the operation of the entire system is not simulated. For instance, improvements in pipe thermal conductivity (from 0.4 to 3 W/(m K)) result in a 7.57% improvement in BHE performance when simulating only a BHE (constant inlet temperature assumed). However, a more realistic simulation of the entire system shows that improvements at a system level are only 0.15%. Other important simulation aspects are also investigated, focusing on different choices regarding the sensitivity analysis method, flow condition type, and optimization strategy. The results suggest that modifications to individual BHE parameters have a limited impact on the overall system performance, while modifying all parameters simultaneously can lead to more significant reductions in total system energy consumption (6% in this study). Furthermore, the research also shows that the potential savings in investment costs (by reducing the borehole depth) outweigh potential savings in operational costs. [ABSTRACT FROM AUTHOR]

On 26 April 2017, Mexico formally adopted the 2030 Sustainable Development Agenda to link the national government to local governments, the private sector, civil society and academia. The restoration of heritage residential buildings (recovery programme) and the construction of new residential buildings aim to create a diversified environment for populations at different economic levels. However, the restoration programme faces conservation challenges. Given this context, this paper presents the results of in-depth, semi-structured interviews conducted with stakeholders in Mexico's heritage and energy sectors. The duration of each interview was approximately two hours, with 52,372 total interview words. The paper identifies prevailing opinions regarding prioritising historical values, energy efficiency, historic buildings and users concerning Mexico City's Historical Centre, a World Heritage Site needing a more appropriate sustainable development plan. Using grounded theory and thematic analysis, the interview data were analysed based on the interrelationship between thermal comfort, energy efficiency and heritage conservation changes over time. The results of this research will strengthen our understanding of the interventions and processes involved in managing and living in this World Heritage Site and its future impacts on buildings. The approach also underscores the importance of how stakeholders prioritise different values in making energy efficiency-promoting decisions and enhances awareness of the decision-making process and actions adopted by heritage building users. Thus, understanding the dynamic interrelationship between values, users and energy could improve the sustainable management of heritage sites and future development. [ABSTRACT FROM AUTHOR]

The wide spread of the Diesel engine has been instrumental in the development of modern shipping. Marine Diesel engines dominate today as an option for the propulsion of commercial ships. While replacing Diesel engines with alternative propulsion engines is difficult to achieve, companies, in light of the new EEXI regulations, are turning to improvements, such as operating at lower rotational speeds, higher maximum combustion pressures, and more efficient overcrowding systems. Τhe purpose of this research paper is (i) to present the basic operating principles of marine Diesel engines, (ii) to study the main differences between electronically controlled Diesel engines and their mechanically controlled counterparts, and (iii) to evaluate their performance under newly introduced IMO's EEXI regulations. Thus, after comparing Wärtsilä RTA and WinGD WX, the paper concluded that WinGD WX, being electronically controlled, will perform more effectively under new EEXI regulations, as it offers (i) reduced fuel consumption in low-load mode, (ii) zero-smoke emission at all operating speeds, (iii) very stable operation at low speed, (iv) more straightforward engine setup leading to less maintenance, (v) more extended periods between maintenance, mainly due to better load distribution between cylinders and more perfect combustion. From a regulatory perspective, the new limitations installed by the newly implemented EEXI and CII regulations will cause fewer implications in electronically controlled engines, while from an economic standpoint, the electronically controlled engines decrease OPEX and require fewer personnel, due to their efficiency at low loads and overall flexibility. [ABSTRACT FROM AUTHOR]

The present study offers a thorough scientometric analysis of the practice of bioclimatic retrofitting in commercial buildings, which is considered a crucial approach for mitigating energy consumption and addressing the challenges posed by climate change. Since Scopus offers advanced tools for literature search and analysis, its database was used to acquire bibliographic data for nearly 400 published papers using a bibliometric search, a scientometric methodology, and an in-depth qualitative analysis. The scientometric procedure utilizes various quantitative dimensions, such as the number of intermediate citations, occurrences, average publication year, and general connection power. A retrospective examination of research publication patterns spanning 2008 to May 2022 has unveiled an upward trajectory in scholarly investigations pertaining to commercial buildings during this 15-year period. Nevertheless, research on the topics of bioclimatic design, retrofitting strategies, and green building practices in the context of commercial buildings exhibits a more gradual incline, displaying an almost linear trend between the years 2016 and 2020. Additionally, the study provides qualitative perspectives on the research environment. The paper delineates various focal points of research within the field, encompassing thermal comfort in the context of energy management and climate control, ventilation systems, sustainable development as it pertains to architectural designs and green buildings, retrofitting strategies in commercial buildings to enhance energy efficiency, the interplay between carbon dioxide levels and energy resources, the utilization of solar energy, the relationship between energy conservation and atmospheric temperature, and the application of building energy simulation techniques. [ABSTRACT FROM AUTHOR]

Buildings consume about 40% of the global energy. Therefore, the building sector plays a key role in achieving the goals of carbon peak and carbon neutrality. Various energy-saving technologies for buildings, such as building envelops, mechanical systems, and energy resources, have been developed to help to achieve zero- or even net-energy buildings while maintaining comfort and a healthy indoor environment. This Special Issue on building energy-saving technology was open to all contributors in the field of building engineering. The original experimental studies, numerical simulations, and reviews in all aspects of building energy utilization, management, and optimization have been considered. For this event, all of these topics were covered in the extensive submissions which were accepted, but interesting papers on other aspects of building energy efficiency were also received. The purpose of this editorial is to summarize the main research findings of the accepted papers in this Special Issue, including the energy-saving technologies involved in building envelops, mechanical systems, and occupant behaviors, and to identify a number of research questions and research directions. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of different battery charging strategies for off-grid solar PV systems. The strategies evaluated include constant voltage charging, constant current charging, PWM charging, and hybrid charging. The performance of each strategy is evaluated based on factors such as battery capacity, cycle life, DOD, and charging efficiency, as well as the impact of environmental conditions such as temperature and sunlight. The results show that each charging strategy has its advantages and limitations, and the optimal approach will depend on the specific requirements and limitations of the off-grid solar PV system. This study provides valuable insights into the performance and effectiveness of different battery charging strategies, which can be used to inform the design and operation of off-grid solar PV systems. This paper concludes that the choice of charging strategy depends on the specific requirements and limitations of the off-grid solar PV system and that a careful analysis of the factors that affect performance is necessary to identify the most appropriate approach. The main needs for off-grid solar photovoltaic systems include efficient energy storage, reliable battery charging strategies, environmental adaptability, cost-effectiveness, and user-friendly operation, while the primary limitations affecting these systems encompass intermittent energy supply, battery degradation, environmental variability, initial investment costs, fluctuations in energy demand, and maintenance challenges, emphasizing the importance of careful strategy selection and system design to address these factors. It also provides valuable insights for designing and optimizing off-grid solar PV systems, which can help to improve the efficiency, reliability, and cost-effectiveness of these systems. [ABSTRACT FROM AUTHOR]

Energy harvesting has become an increasingly important field of research as the demand for portable and wearable devices continues to grow. Skin-contact triboelectric nanogenerator (TENG) technology has emerged as a promising solution for energy harvesting and motion sensing. This review paper provides a detailed overview of skin-contact TENG technology, covering its principles, challenges, and perspectives. The introduction begins by defining skin-contact TENG and explaining the importance of energy harvesting and motion sensing. The principles of skin-contact TENG are explored, including the triboelectric effect and the materials used for energy harvesting. The working mechanism of skin-contact TENG is also discussed. This study then moves onto the applications of skin-contact TENG, focusing on energy harvesting for wearable devices and motion sensing for healthcare monitoring. Furthermore, the integration of skin-contact TENG technology with other technologies is discussed to highlight its versatility. The challenges in skin-contact TENG technology are then highlighted, which include sensitivity to environmental factors, such as humidity and temperature, biocompatibility and safety concerns, and durability and reliability issues. This section of the paper provides a comprehensive evaluation of the technological limitations that must be considered when designing skin-contact TENGs. In the Perspectives and Future Directions section, this review paper highlights various advancements in materials and design, as well as the potential for commercialization. Additionally, the potential impact of skin-contact TENG technology on the energy and healthcare industries is discussed. [ABSTRACT FROM AUTHOR]

Currently, the Global Positioning System (GPS) is widely used, but its signal is attenuated by factors such as trees, walls, and ceilings, which severely degrade its positioning accuracy. To fill the gap, various indoor positioning techniques have attracted increasing attention in recent years. Visible light positioning (VLP) is a promising scheme for indoor positioning due to its high precision, high security, and low energy consumption; however, ubiquitous diffuse reflection affects the accuracy and robustness of VLP. During our testing, we found that diffuse reflection could increase the error in RSS values by 20~30%, severely affecting VLP accuracy; however, diffuse reflection is inevitable in real positioning environments. To solve this problem, this paper first establishes a wall diffuse reflection model and then implements a visible light positioning system based on an Internet of Things platform. Finally, this paper uses the system to verify the effectiveness of the diffuse reflection model. The experiments show that the proposed model effectively improves positioning accuracy by 36.7~61.3%. [ABSTRACT FROM AUTHOR]

The availability of empirical energy data from Advanced Metering Infrastructure (AMI)—which includes household smart meters—has enabled residential energy demand to be characterised in different forms. This paper first presents a literature review of applications of measured electricity, gas, and heat consumption data at a range of temporal resolutions, which have been used to characterise and develop an understanding of residential energy demand. User groups, sectors, and policy areas that can benefit from the research are identified. Multiple residential energy demand datasets have been collected in the UK that enable this characterisation. This paper has identified twenty-three UK datasets that are accessible for use by researchers, either through open access or defined processes, and presents them in an inventory containing details about the energy data type, temporal and spatial resolution, and presence of contextual physical and socio-demographic information. Thirteen applications of data relating to characterising residential energy demand have been outlined in the literature review, and the suitability of each of the twenty-three datasets was mapped to the thirteen applications. It is found that many datasets contain complementary contextual data that broaden their usefulness and that multiple datasets are suitable for several applications beyond their original project objectives, adding value to the original data collection. [ABSTRACT FROM AUTHOR]

This paper is dedicated to research of the impact of the limited heat source capacity on indoor temperature and energy consumption in serial nZEB residential buildings. This is an innovative aspect as it explores the potential design simplification for different locations, allowing for cost optimization and quicker construction timelines. The objective of this paper is to examine the impact of limited heat source capacity by utilizing thermal mass and optimizing the ventilation operation. Numerical results demonstrate that incorporating thermal mass increases heating energy consumption by up to 1%. The study addresses the impact of limited heating capacity on indoor temperatures and the need to manage ventilation's impact during peak temperatures using simulation software IDA ICE. The study reveals that a limited heating capacity reduces energy consumption up to 2.6%, but may result in lower indoor temperatures. By optimizing ventilation strategies, energy consumption can be reduced from 2.4% to 4.4% compared to the suboptimal solution, and from 2.8% to 6.5% compared to the initial case. Parametric analysis reveals optimal ventilation operation change-over point at an outdoor temperature of −17 °C during winter. The research provides practical recommendations for adjusting heating schedules, selecting appropriate heating capacities and implementing optimal ventilation strategies. [ABSTRACT FROM AUTHOR]

The world is undergoing an irreversible shift towards clean energy. Microgrids are recognized as a key technology that holds significant potential to make a substantial difference in this regard. The paper provides a comprehensive overview of how microgrids work and their impact on climate. The research presented in this paper focuses on reducing carbon dioxide (CO2) in the main campus of Qassim University, Saudi Arabia, through the development and implementation of an engineering model that facilitates the installation of a microgrid system designed to meet the university's sustainability goals. The study aims to explore possible solutions that can reduce emissions in the administrative building (A7) at Qassim University and meet the university environmental plan. Therefore, a comprehensive study is conducted to investigate the potential reduction in emissions associated with the installation of a microgrid system. This microgrid system operates in a grid-connected mode and comprises three main components: the load, a photovoltaic (PV) system, and batteries. The results of the study indicate that the microgrid reveals a notable transition in the primary sources of electricity. Moreover, the microgrid system proves its capability to meet a substantial portion of the daily energy requirements, highlighting its efficiency and effectiveness in addressing energy needs. The findings of this study highlight the significant potential of the proposed model in curbing carbon emissions, as it demonstrates a reduction from 615.8 to 147.4 Mt of CO2. This reduction aligns with the university's commitment to sustainability and green initiatives. The computed decrease in carbon footprint emphasizes the possibility of the suggested model to encourage sustainable practices among the university community and mitigate the environmental consequences of energy usage. [ABSTRACT FROM AUTHOR]

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption. [ABSTRACT FROM AUTHOR]

Efficient and low-carbon energy utilization is a crucial aspect of promoting green and sustainable development. Multi-energy microgrids, which incorporate multiple interchangeable energy types, offer effective solutions for low-carbon and efficient energy consumption. This study aims to investigate the sharing of energy and carbon in multiple multi-energy microgrids (MMEMs) to enhance their economic impact, low-carbon attributes, and the efficient utilization of renewable energy. In this paper, an energy–carbon co-sharing operation model is established, incorporating carbon capture systems (CCSs) and two-stage power-to-gas (P2G) devices within the MMEMs to actualize low-carbon operation. Furthermore, based on cooperative game theory, this paper establishes an energy–carbon co-sharing Nash negotiation model and negotiates based on the energy–carbon contribution of each subject in the cooperation as bargaining power so as to maximize both the benefits of the MMEM alliance and the distribution of the cooperation benefits. The case study results show that the overall benefits of the alliance can be increased through Nash negotiation. Energy–carbon co-sharing can effectively increase the renewable energy consumption rate of 8.34%, 8.78%, and 8.83% for each multi-energy microgrid, and the overall carbon emission reduction rate reaches 17.81%. Meanwhile, the distribution of the benefits according to the energy–carbon co-sharing contribution capacity of each entity is fairer. [ABSTRACT FROM AUTHOR]

The paper introduces the development stages of a MOSFET-based controller for a DC brush motor. The main objective was to design a controller that could be integrated with the existing telemetry system, offering full configurability through an Android application. This controller aims to provide real-time analysis of data collected from the measurement system, including motor revolutions and current draw. Based on the analyzed data and the conditions set in the Android application, the controller adjusts the motor's operational characteristics accordingly. The paper provides a comprehensive description of the controller system's functioning. The proposed control system is particularly relevant in applications where minimizing energy consumption for driving a DC motor is of utmost importance. [ABSTRACT FROM AUTHOR]

Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for autonomous environment perception systems. For applications on mobile devices, current research focuses on energy-efficient segmenters for both frame and event-based cameras. However, there is currently no artificial neural network (ANN) that can perform efficient segmentation on both types of images. This paper introduces spiking neural network (SNN, a bionic model that is energy-efficient when implemented on neuromorphic hardware) and develops a Spiking Context Guided Network (Spiking CGNet) with substantially lower energy consumption and comparable performance for both frame and event-based images. First, this paper proposes a spiking context guided block that can extract local features and context information with spike computations. On this basis, the directly-trained SCGNet-S and SCGNet-L are established for both frame and event-based images. Our method is verified on the frame-based dataset Cityscapes and the event-based dataset DDD17. On the Cityscapes dataset, SCGNet-S achieves comparable results to ANN CGNet with 4.85 × energy efficiency. On the DDD17 dataset, Spiking CGNet outperforms other spiking segmenters by a large margin. [ABSTRACT FROM AUTHOR]

Planning for the eventual electrification of transport in sub-Saharan Africa requires accurate simulation of its unique transport systems. The few studies that attempt to model electric minibus taxis—vehicles extensively used in sub-Saharan Africa's public transport systems—vary greatly in their results. This paper analyses, compares and corrects the only two existing studies that project energy consumption of electric minibus taxis in the region. One of the studies projected an energy consumption of 0.39 kWh/km, while the other projected 0.93 kWh/km. This paper carefully analyses the simulation tools and models and cumulatively applies corrections from the literature and scientific analyses. As a result, the discrepancy between the two simulation tools is eliminated for a given data input and a final energy consumption is estimated in the range of 0.49–0.52 kWh/km, depending on the input data. [ABSTRACT FROM AUTHOR]

The transport sector is the second leading emitter of greenhouse gas emissions (GHGEs) from fuel combustion activities on a global scale. Reducing emissions related to this sector requires detailed data about the emissions by vehicle type and traveled distance. Generally, these data are missing in developing countries, which makes difficult the establishment of effective policies for the reduction of these emissions. This paper presents a hybrid method to estimate these emissions using the IPCC 2006 guidelines. The method combines bottom-up and top-down approaches to estimate vehicular emissions using data about the vehicle type, vehicle kilometers traveled, and fuel consumption. This method is applied for the first time for the Palestinian territory. Data have been collected from the administration, official reports, and papers. The results show a significant increase in the total vehicles in Palestine, particularly diesel vehicles. Emissions from the on-road transport system were approximately 2,207,834 tons of CO2eq in 2019. Diesel vehicles were responsible for about 75% of these emissions. Private cars were the most significant contributor to these emissions, with a share exceeding 50% of the total emissions, followed by commercial vehicles and light trucks (20%), public taxis (9%), and buses (7%). These results show clearly that the GHGEs reduction policies in Palestine should focus on diesel and private vehicles by developing the public transport systems and replacing diesel and gasoline vehicles with more environmentally friendly vehicles, such as hybrid and electric cars. [ABSTRACT FROM AUTHOR]

The aim of this article is to present solutions related to wind energy in EU countries as an alternative to fossil fuels. This article is based on secondary information and statistical data regarding the development of wind power engineering in EU countries for the years 2016–2022. The main purpose of this paper is to analyze of the relations between the development of wind energy in European Union countries and GPD (gross domestic product) per capita and selected factors. The following hypotheses were formulated: H1—There is a statistically significant correlation between GDP per capita and the use of wind energy in European Union countries. H2—There is a relationship between the length of the coastline and the use of wind energy in European Union countries. H3—There is a statistically significant correlation between the attitude to uncertainty of the inhabitants of a given country and the use of wind energy in said country. The presented research results support all these hypotheses. The results of the research regarding H2 are as follows: in the case of northern European countries (Ireland and Finland) and the Iberian Peninsula (Spain and Portugal), the development of wind power engineering in the study period was faster than could be inferred from the length of the coastline in these countries. Regarding hypothesis H1, it was concluded on the basis of the analysis that the involvement of countries in the development of wind power engineering is correlated with their wealth. The novelty of this paper emerges from its innovative approach to analyzing wind power engineering, its incorporation of cultural factors, its quantitative assessment of correlations, and its actionable policy recommendations. These elements collectively contribute to a comprehensive and impactful study that advances our understanding of wind energy adoption in the European Union. [ABSTRACT FROM AUTHOR]

DC power may be more efficient than AC power in certain applications, especially when it comes to local generation and storage. This is because AC power requires extra equipment to convert it to DC power, which can lead to energy losses. Using DC power, on the other hand, makes it easier for devices to use it directly, resulting in higher energy efficiency. Additionally, using DC power can reduce equipment capital costs as it eliminates the need for additional AC–DC conversion equipment. Finally, DC power systems can offer new communication capabilities, including plug-and-play for generation and storage devices, making it simpler to integrate these systems into existing infrastructure. This paper analyzes the optimal size of a photovoltaic/PEM fuel cell system to supply a certain DC commercial load in NEOM city. To identify the best size of the PV/PEMFC, minimizing the cost of energy (COE) and minimizing the net present cost (NPC) are considered. The paper studies three sizes of PEMFCs: 15 kW, 20 kW, and 25 kW. In addition, five different PV modules are selected: Axitec 450 Watt, Jinko 415 Watt, REC Solar 410 Watt, Seraphim 310 Watt, and Tongwei 415 Watt. The results of the study confirmed that the best size of the hybrid system comprises a 15 kW PEMFC, a 267 kW Tongwei PV array, a 60 kg electrolyzer, and a 20 kg hydrogen tank. Under these conditions, the COE and NPC are 0.293 USD/kWh and 498,984 USD, respectively. [ABSTRACT FROM AUTHOR]

With population and income growth, the need for energy has increased in developing and emerging economies, which has inevitably led to an increase in carbon dioxide emissions (CO2e). This paper investigates the impact of energy consumption on CO2e influenced by population growth, energy consumption per capita, and income. In particular, this paper investigates whether or not an increase in energy consumption, energy intensity, energy consumption per capita, population growth, and income impacts CO2e in China, India, and the USA. The study applied the non-linear Autoregressive distributed lag (NARDL) and machine learning techniques. We found a significant impact of energy consumption per capita on the CO2 emissions in China, India, and USA. Furthermore, the results revealed that, when income increased, CO2 emissions increased in India, but decreased in the USA. The results confirmed that population growth increases CO2 emissions only in India. The results revealed that a decrease in energy intensity significantly improves the environmental quality in China and India. Finally, we forecasted the CO2e trend from 2017 to 2025. The results revealed an upcoming increase in CO2e levels in China and India. Conversely, the forecasted results demonstrated a downward trend of CO2e emissions in the USA. [ABSTRACT FROM AUTHOR]

In the era of intelligent applications, Mobile Edge Computing (MEC) is emerging as a promising technology that provides abundant resources for mobile devices. However, establishing a direct connection to the MEC server is not always feasible for certain devices. This paper introduces a novel Device-to-Device (D2D)-assisted system to address this challenge. The system leverages idle helper devices to execute and offload tasks to the MEC server, thereby enhancing resource utilization and reducing offload time. To further minimize offloading time for latency-sensitive tasks, this paper incorporates edge caching. The problem is formulated by jointly optimizing computation, communication and caching, and a novel Joint Multiple Decision Optimization Algorithm (JMDOA) is proposed to solve the minimum-energy-consumption problem. Specifically, the JMDOA algorithm decomposes the integer-mixed non-convex optimization problem into two subproblems based on distinct properties of discrete variables. These subproblems are solved separately and optimized iteratively, ensuring convergence to a suboptimal solution. Simulations demonstrate the effectiveness and superiority of JMDOA, exhibiting lower energy consumption and reduced time compared to other baseline algorithms, approaching the optimum. This work contributes to the field by presenting a novel approach to optimizing resource allocation in MEC systems, with potential implications for the future development of intelligent applications. [ABSTRACT FROM AUTHOR]

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid electric vehicle, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICE) and the electric motor. The study described herein also presents the influence of factors such as the ambient temperature, vehicle speed and traffic distance on the PHEV's energy consumption. It has been shown that the vehicle's range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in everyday driving conditions. When analyzing the energy consumption based on the vehicle's unitary energy consumption model, attention was paid to values that are strongly correlated with traffic and weather conditions. In addition, the authors emphasized that the estimated total energy consumption of a battery electric vehicle (BEV) or hybrid vehicle (PHEV), relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. As shown in this paper, the energy consumption intensity of a vehicle is significantly influenced not only by its speed but also by weather conditions, including ambient temperature. In extreme cases, energy consumption intensity can increase by up to 68% relative to a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) cycle. [ABSTRACT FROM AUTHOR]

Energy crises and the growth of the energy demand have increased the interest in utilizing unconventional power sources. Thus, renewable energy sources have become a topic of interest to mitigate rising energy concerns and cope with increased electricity demand. With remarkable merits including cleanness and abundance, photovoltaic (PV) solar energy systems are a key to solving these issues. The employed inverters should effectively utilize the maximum available power from the PV solar system and transfer this power to the utility grid without posing any further limitations. However, the unequal power generation of different PV systems caused by partial shading (PS) and other PV panel degradation factors leads to a reduction in generation capacity. One of the relatively new solutions to mitigate the mismatch concerns between the PV modules and sub-modules is to extract the maximum power of each sub-module individually. The main objective of this paper is to present a comprehensive review of such PV grid-connected inverters topologies associated with sub-module connection and control. It will classify the PV grid-tied inverters in accordance with the level where the maximum power point tracking (MPPT) system is implemented. A special focus has been placed on sub-module microinverters (MI) in terms of circuit topologies, conversion efficiency, and controller design. This paper provides a comprehensive analysis of employing the distributed MPPT (DMPPT) approach to maximize the power generation of PV systems by mitigating the mismatch issues inside the PV module. The circuit topology, PV system configuration, and MPPT algorithms used for applying DMPPT solutions in PV SMs are discussed in detail in this study. [ABSTRACT FROM AUTHOR]

The large-scale integration of renewable energy into the grid has led to a gradual diversification of power generation trading units on the power-side, with varying operational characteristics, costs, and trading needs among diverse power generation trading units. Traditional power system clearing models face challenges. Block order is a bidding type that allows for multiple volume–price combinations. At the same time, the randomness and volatility of renewable energy poses challenges to the safe and stable operation of the system. Building a power system clearing model that meets the diverse and flexible needs of the power system has become an important consideration. Therefore, this paper considers the design of three flexible energy blocks: a sustaining block, flux block, and adjustment block to meet the differential needs of diverse trading units and establishes a flexible block order clearing model. Secondly, it establishes a joint clearing model of electrical energy and frequency modulation (FM) to ensure the stable and reliable operation of the system, and solves the model based on relevant constraints to determine the winning electrical energy price and FM price. The CPLEX and Yalmip algorithms are used to solve the model. Finally, a case study was conducted based on an improved IEEE 14-bus system. The results showed that the model proposed in this paper can satisfy the differential needs of diverse trading units, effectively improve the renewable energy consumption capacity, and reduce the prices of the energy market and frequency modulation market. Also, the standard deviation of the net load of the system is relatively low, which improves the reliability of the power system's operation. [ABSTRACT FROM AUTHOR]

One of the 10 priorities of the Green New Deal is sustainable transportation. It should be considered in the perspective of long-term ecological and economic sustainability, according to the trend of opportunities for sustainable development. The economic and environmental aspects of transportation related to the harvesting and movement of timber play a special role in the energy cost and their environmental impact per distance of raw material supply. The principles of rational and energy-efficient use of transportation equipment play a key role in the movement of raw timber. These are influenced by the availability of timber resources, limitation of vehicle payloads, reduction of energy consumption expressed in terms of reduction of fuel combustion, or current legal and road regulations for timber transportation. The paper evaluates economic and environmental factors in relation to the demand for fuels necessary for the implementation of road transportation of softwood timber. The aim of this paper was to present the current situation of the use of transportation means in the movement of bulky timber, as well as opportunities to optimize fuel consumption and CO2 emissions, affecting the economic and environmental effects. Previous studies on fuel consumption in the transportation of roundwood indicate irrational use of vehicles. This indicates unused payload capacity increasing energy inputs expressed in fuel consumption. It has been empirically investigated that this is the result of improperly approved transport sets adapted to the material being transported. In addition, it was shown that there is a clear correlation between the vehicle payload utilization rate and economic and environmental factors. The results of this study point to the potential to increase the use of transportation vehicles for timber transportation and reduce fuel combustion and CO2 emissions by 7–20%. [ABSTRACT FROM AUTHOR]

This paper proposes the method pELECTRE Tri, a novel probabilistic Multi-Criteria Decision Making (MCDM) method using the traditional ELECTRE Tri, probability distribution, and Monte Carlo simulation to support informed decision-making in complex and uncertain decision environments. The proposed method is illustrated through a case study involving the renovation of three social housing buildings. The paper provides new insights into the application of probabilistic sorting MCDM in the context of energy efficiency in buildings and highlights the benefits of using probabilities rather than crisp values to categorize alternatives enabling stakeholders to make better use of available resources, especially when dealing with a large dataset of energy measures with different features. The methodology implemented in Python (DOI: 10.5281/zenodo.7967655) is available as an open source. [ABSTRACT FROM AUTHOR]

Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models' predictive performance and application status. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), the number of devices will also increase tremendously. However, we need more wireless communication resources. It has been shown in the literature that non-orthogonal multiple access (NOMA) offers high multiplexing gains due to the simultaneous transfer of signals, and massive multiple-input–multiple-outputs (mMIMOs) offer high spectrum efficiency due to the high antenna gain and high multiplexing gains. Therefore, a downlink mMIMO NOMA cooperative system is considered in this paper. The users at the cell edge in 5G cellular system generally suffer from poor signal quality as they are far away from the BS and expend high battery power to decode the signals superimposed through NOMA. Thus, this paper uses a cooperative relay system and proposes the mMIMO NOMA double-mode model to reduce battery expenditure and increase the cell edge user's energy efficiency and sum rate. In the mMIMO NOMA double-mode model, two modes of operation are defined. Depending on the relay's battery level, these modes are chosen to utilize the system's energy efficiency. Comprehensive numerical results show the improvement in the proposed system's average sum rate and average energy efficiency compared with a conventional system. In a cooperative NOMA system, the base station (BS) transmits a signal to a relay, and the relay forwards the signal to a cluster of users. This cluster formation depends on the user positions and geographical restrictions concerning the relay equipment. Therefore, it is vital to form user clusters for efficient and simultaneous transmission. This paper also presents a novel method for efficient cluster formation. [ABSTRACT FROM AUTHOR]