Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

High Energy Efficiency Dynamic Connected Hybrid Precoding for mm Wave Massive MIMO Systems

This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC algorithm.Firstly,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation problem.Thus,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound method.Then,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is one-to-one.Simulation results show that the HEDC structure enjoys low power consumption and satisfactory spectral efficiency.The proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.

Cooperative activation of sodium channels for downgrading the energy efficiency in neuronal information processing

The Hodgkin–Huxley model assumes independent ion channel activation,although mutual interactions are common in biological systems.This raises the problem why neurons would favor independent over cooperative channel activation.In this study,we evaluate how cooperative activation of sodium channels affects the neuron’s information processing and energy consumption.Simulations of the stochastic Hodgkin–Huxley model with cooperative activation of sodium channels show that,while cooperative activation enhances neuronal information processing capacity,it greatly increases the neuron’s energy consumption.As a result,cooperative activation of sodium channel degrades the energy efficiency for neuronal information processing.This discovery improves our understanding of the design principles for neural systems,and may provide insights into future designs of the neuromorphic computing devices as well as systematic understanding of pathological mechanisms for neural diseases.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

Spatiotemporal variations,influencing factors,and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency(EE)helps address energy constraints and promote environmental sustainability.This study comprehensively explores the spatiotemporal variations,influencing factors,and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier(G-S-MinDS),exploratory spatial data analysis(ESDA),multiscale geographically weighted regression(MGWR),and fuzzy set qualitative comparative analysis(fsQCA)methods.The findings are:①China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%,showing considerable promotion potential.②Industrial structure optimization,population agglomeration,economic development,and increased green coverage contribute positively,while government intervention and openness hinder China’s urban EE.③Four configurational promotion paths for enhancing China’s urban EE are identified,where among those paths population density is a core condition,while government intervention is not.This study provides valuable insights into substantially improving urban EE,emphasizing the need for targeted policies to address energy and environmental crises in China.

Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.

Evaluation of Energy Efficiency and Analysis of Influencing Factors of Company CW’s Manufacturing Workshops

In this work,the Slacks-Based Measure(SBM)model within Data Envelopment Analysis was employed to establish a set of indicators for evaluating the energy efficiency of manufacturing workshops.The energy efficiency of 12 Company CW’s manufacturing workshops from 2016 to 2022 was assessed.The findings indicated that aside from a few workshops operating at the production frontier,the rest exhibit significant fluctuations in energy efficiency and generally low energy efficiency.Subsequently,a combined GRA-Tobit analysis model was introduced to identify factors influencing the energy efficiency of Company CW’s manufacturing workshops.Regression analysis revealed that technological investments,employee quality,workshop production scale,investment in clean energy,and the level of pollution control all significantly impact the energy efficiency of Company CW’s manufacturing workshops.By evaluating the energy efficiency of Company CW’s manufacturing workshops and studying their influencing factors,this research aids company managers in understanding the energy efficiency of the manufacturing process.It optimizes the combination of various production elements,thereby offering effective guidance for improving the energy efficiency issues of the company’s manufacturing workshops,which can contribute to enhancing the corporation’s overall energy efficiency.

Research on the Impact of Digital Finance on Energy Efficiency in China

Digital finance and green technology innovation(GTI)serve as powerful engines for promoting energy efficiency(EE)and economic development.This paper explores the mechanism by which digital finance impacts EE based on panel data from 30 provinces in China spanning from 2011 to 2019.The results demonstrate that digital finance can significantly enhance EE,with a particularly pronounced effect in the eastern region.Through mechanistic analysis,it is evident that GTI serves as the transmission pathway through which digital finance influences EE,accounting for 45.3%of the effect.The policy implication of this study suggests that China should expedite the digitization of financial markets to further harness the development of digital finance,particularly in pursuit of its technological innovation and green,lowcarbon environmental protection effects.

Meta-analysis of CO_(2) conversion,energy efficiency,and other performance data of plasma-catalysis reactors with the open access PIONEER database

This paper brings the comparison of performances of CO_(2)conversion by plasma and plasma-assisted catalysis based on the data collected from literature in this field,organised in an open access online database.This tool is open to all users to carry out their own analyses,but also to contributors who wish to add their data to the database in order to improve the relevance of the comparisons made,and ultimately to improve the efficiency of CO_(2)conversion by plasma-catalysis.The creation of this database and database user interface is motivated by the fact that plasma-catalysis is a fast-growing field for all CO_(2)conversion processes,be it methanation,dry reforming of methane,methanolisation,or others.As a result of this rapid increase,there is a need for a set of standard procedures to rigorously compare performances of different systems.However,this is currently not possible because the fundamental mechanisms of plasma-catalysis are still too poorly understood to define these standard procedures.Fortunately however,the accumulated data within the CO_(2)plasma-catalysis community has become large enough to warrant so-called“big data”studies more familiar in the fields of medicine and the social sciences.To enable comparisons between multiple data sets and make future research more effective,this work proposes the first database on CO_(2)conversion performances by plasma-catalysis open to the whole community.This database has been initiated in the framework of a H_(2)0_(2)0 European project and is called the“PIONEER Data Base”.The database gathers a large amount of CO_(2)conversion performance data such as conversion rate,energy efficiency,and selectivity for numerous plasma sources coupled with or without a catalyst.Each data set is associated with metadata describing the gas mixture,the plasma source,the nature of the catalyst,and the form of coupling with the plasma.Beyond the database itself,a data extraction tool with direct visualisation features or advanced filtering functionalities has been developed and is available online to the public.The simple and fast visualisation of the state of the art puts new results into context,identifies literal gaps in data,and consequently points towards promising research routes.More advanced data extraction illustrates the impact that the database can have in the understanding of plasma-catalyst coupling.Lessons learned from the review of a large amount of literature during the setup of the database lead to best practice advice to increase comparability between future CO_(2)plasma-catalytic studies.Finally,the community is strongly encouraged to contribute to the database not only to increase the visibility of their data but also the relevance of the comparisons allowed by this tool.

Peak Electricity Demand Management and Energy Efficiency among Large Steel Manufacturing Firms in Nairobi Region, Kenya

To reduce peak electricity demand and hence reduce capacity costs due to added investment of generating additional power to meet short intervals of peak demand, can enhance energy efficiency. Where it is possible to adjust timing and the quantity of electricity consumption and at the same time achieve the same useful effect, the value of the energy service itself remains unchanged. Peak demand management is viewed as the balance between demand and generation of energy hence an important requirement for stabilized operation of power system. Therefore, the purpose of this study was to establish the correlation between peak electricity demand management strategies and energy efficiency among large steel manufacturing firms in Nairobi, Kenya. The strategies investigated were demand scheduling, Peak shrinking and Peak shaving. Demand scheduling involves shifting predetermined loads to low peak periods thereby flattening the demand curve. Peak shrinking on the other hand involves installation of energy efficient equipment thereby shifting the overall demand curve downwards. Peak shaving is the deployment of secondary generation on site to temporarily power some loads during peak hours thereby reducing demand during the peak periods of the plant. The specific objectives were to test the relationship between demand scheduling and energy efficiency among large steel manufacturing firms in Nairobi Region;to test the correlation between peak shrinking and energy efficiency among large steel manufacturing firms in Nairobi Region;and to test the association between peak shaving and energy efficiency among large steel manufacturing firms in Nairobi Region. The study adopted a descriptive research design to determine the relationship between each independent variable namely demand scheduling, peak shrinking, peak shaving and the dependent variable, the energy efficiency. The target population was large steel manufacturing firms in Nairobi Region, Kenya. The study used both primary and secondary data. The primary data was from structured questionnaires while secondary data was from historical electricity consumption data for the firms under study. The results revealed that both peak shrinking and peak shaving were statistically significant in influencing energy efficiency among the steel manufacturing firms in Nairobi Region, each with Pearson correlation coefficient of 0.903, thus a strong linear relationship between the investigated strategy and the dependent variable, energy efficiency. The obtained results are significant at probability value of 0.005 (p 0.05). The conclusion is that peak shrinking and peak shaving have an impact on energy efficiency in the population under study, and if properly implemented, may lead to efficient utilization of the available energy. The study further recommended that peak demand management practices need to be implemented efficiently as a way of improving the overall plant load factor and energy efficiency.

Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network

To meet the key performance requirement of the 5G network and the demand of the growing number of mobile subscribers,millions of base stations are being constructed.5G New Radio is designed to enable denser network deployments,which raises significant concerns about network energy consumption.Machine learning(ML),as a kind of artificial intelligence(AI)technologies,can enhance network optimization performance and energy efficiency.In this paper,we propose AI/ML-assisted energy-saving strategies to achieve optimal performance in terms of cell shutdown duration and energy efficiency.To realize network intelligence,we put forward the concept of intrinsic AI,which integrates AI into every aspect of wireless communication networks.

Impact of Energy Efficiency and Financial Support on Green Upgrading in China’s Industrial Sector

The report from the 20th National Congress of China emphasizes the importance of focusing on the clean,low-carbon,and efficient use of energy,increasing financial support,and promoting green upgrading within the industrial sector.This paper,based on annual data,employs the entropy weight method to construct a comprehensive index reflecting the impact of green upgrading in industrial sectors.To delve deeper,it utilizes the DEA model to measure energy efficiency and its subdivision BCC model to break down energy efficiency into technical and scale efficiency.The financial support landscape is examined from the vantage points of both direct and indirect financing.Using a multivariate time series model,this paper thoroughly investigates the influence of energy efficiency and financial support on the green upgrading of the industrial sector.The findings reveal a significant positive impact of both energy efficiency and financial support on green upgrading in industrial industries.Notably,scale efficiency emerges as the primary driver of energy efficiency.Moreover,indirect financing proves to be more effective in promoting financial support than direct financing.The empirical results retain their robustness even after substituting explanatory variables.The study concludes by contextualizing the research findings within the current real-world scenario,offering practical insights,and proposing specific recommendations.

Priority Based Energy Efficient MAC Protocol by Varying Data Ratefor Wireless Body Area Network

Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.

Low-Cost Insulation for Energy Efficient Buildings in Terai Region of Nepal

There is a huge amount of energy savings potential in public building sector that has yet to be realized.By prioritizing energy efficiency in its own buildings and thus promoting the development of required knowledge in terms of new technology and construction methods,the public sector will lead the way in efforts to increase the rate of renovations.The low-cost insulation strategies and a comparison of cost with existing insulation materials has been described in this study.We have repeatedly faced energy crises and will continue to do so in the future if appropriate action is not taken in a timely manner.Properly implementing energy-saving initiatives in for achieving thermal comfort in buildings as well as reducing the energy costs would undoubtedly inspire the residential sector,resulting in significant reductions in energy usage.Simulations were carried out to study insulation layers on various building components like exterior walls,floor and roofs,generating different scenarios for a building as a base model,which were then compared and analysed to verify the literature used to develop the cases.The proposed recommendations,which have been validated,are certain to increase building energy efficiency,achieve thermal comfort in low cost than what is currently being used.

Industrialization,Efficiency and Social Housing:Sustainable Prefabrication for the Colombian Social and Cultural Context

This research aims to generate processes of industrialization and qualification of social housing in Colombia through prefabrication,low energy consumption and high environmental quality to reduce the current deficit and improve the quality of life in communities with fewer economic resources.Modular coordination and the use of materials with thermal behaviors,appropriate to the country’s climatic characteristics,reduce production costs,avoid waste and improve the architectural and environmental quality of social housing.Passive cooling strategies,such as controlling solar incursion and generating natural cross ventilation eliminate energy consumption and allow reaching desired standards of comfort.In a context in which traditional construction systems have still not resolved the social housing deficit in Colombia and which generate high costs for energy consumption in the search for thermal comfort,industrialized and efficient construction with high environmental quality provides solutions according to the economic,geographic,social and cultural context in Colombia.

Reducing voltage hysteresis of metal oxide anodes to achieve high energy efficiency for Li-ion batteries

In the past two decades,a lot of high-capacity conversion-type metal oxides have been intensively studied as alternative anode materials for Li-ion batteries with higher energy density.Unfortunately,their large voltage hysteresis(0.8-1.2 V) within reversed conversion reactions results in huge round-trip inefficiencies and thus lower energy efficiency(50%-75%) in full cells than those with graphite anodes.This remains a long-term open question and has been the most serious drawback toward application of metal oxide anodes.Here we clarify the origins of voltage hysteresis in the typical SnO2anode and propose a universal strategy to minimize it.With the established in situ phosphating to generate metal phosphates during reversed conversion reactions in synergy with boosted reaction kinetics by the added P and Mo,the huge voltage hysteresis of 0.9 V in SnO_(2),SnO_(2)-Mo,and 0.6 V in SnO2-P anodes is minimized to 0.3 V in a ternary SnO_(2)-Mo-P(SOMP) composite,along with stable high capacity of 936 mA h g^(-1)after 800 cycles.The small voltage hysteresis can remain stable even the SOMP anode operated at high current rate of10 A g^(-1)and wide-range temperatures from 60 to 30℃,resulting in a high energy efficiency of88.5% in full cells.This effective strategy to minimize voltage hysteresis has also been demonstrated in Fe2O3,Co3O4-basded conversion-type anodes.This work provides important guidance to advance the high-capacity metal oxide anodes from laboratory to industrialization.

Dietary supplementation with xylooligosaccharides and exogenous enzyme improves milk production,energy utilization efficiency and reduces enteric methane emissions of Jersey cows

Background Sustainable strategies for enteric methane(CH_(4))mitigation of dairy cows have been extensively explored to improve production performance and alleviate environmental pressure.The present study aimed to investigate the effects of dietary xylooligosaccharides(XOS)and exogenous enzyme(EXE)supplementation on milk production,nutrient digestibility,enteric CH_(4) emissions,energy utilization efficiency of lactating Jersey dairy cows.Forty-eight lactating cows were randomly assigned to one of 4 treatments:(1)control diet(CON),(2)CON with 25 g/d XOS(XOS),(3)CON with 15 g/d EXE(EXE),and(4)CON with 25 g/d XOS and 15 g/d EXE(XOS+EXE).The 60-d experimental period consisted of a 14-d adaptation period and a 46-d sampling period.The enteric CO_(2)and CH_(4) emissions and O2 consumption were measured using two GreenFeed units,which were further used to determine the energy utilization efficiency of cows.Results Compared with CON,cows fed XOS,EXE or XOS+EXE significantly(P<0.05)increased milk yield,true protein and fat concentration,and energy-corrected milk yield(ECM)/DM intake,which could be reflected by the significant improvement(P<0.05)of dietary NDF and ADF digestibility.The results showed that dietary supplementation of XOS,EXE or XOS+EXE significantly(P<0.05)reduced CH_(4) emission,CH_(4)/milk yield,and CH_(4)/ECM.Furthermore,cows fed XOS demonstrated highest(P<0.05)metabolizable energy intake,milk energy output but lowest(P<0.05)of CH_(4) energy output and CH_(4) energy output as a proportion of gross energy intake compared with the remaining treatments.Conclusions Dietary supplementary of XOS,EXE or combination of XOS and EXE contributed to the improvement of lactation performance,nutrient digestibility,and energy utilization efficiency,as well as reduction of enteric CH_(4) emissions of lactating Jersey cows.This promising mitigation method may need further research to validate its long-term effect and mode of action for dairy cows.

Hydrogen production at intermediate temperatures with proton conducting ceramic cells:Electrocatalytic activity,durability and energy efficiency

Proton conducting ceramic cells(PCCs)are an attractive emerging technology operating in the intermediate temperature range of 500 to 700℃.In this work,we evaluate the production of hydrogen at intermediate temperatures by proton conducting ceramic cell electrolysis(PCCEL).We demonstrate a highperformance steam electrolysis owing to a composite positrode based on BaGd_(0.8)La_(0.2)Co_(2)O_(6-δ)(BGLC1082)and BaZr0.5Ce0.4Y0.1O3-δ(BZCY541).The high reliability of PCCEL is demonstrated for 1680 h at a current density as high as-0.8 A cm^(-2)close to the thermoneutral cell voltage at 600℃.The electrolysis cell showed a specific energy consumption ranging from 54 to 66 kW h kg^(-1)that is comparable to state-of-the-art low temperature electrolysis technologies,while showing hydrogen production rates systematically higher than commercial solid oxide ceramic cells(SOCs).Compared to SOCs,the results verified the higher performances of PCCs at the relevant operating temperatures,due to the lower activation energy for proton transfer comparing with oxygen ion conduction.However,because of the p-type electronic conduction in protonic ceramics,the energy conversion rate of PCCs is relatively lower in steam electrolysis.The faradaic efficiency of the PCC in electrolysis mode can be increased at lower operating temperatures and in endothermic conditions,making PCCEL a technology of choice to valorize high temperature waste heat from industrial processes into hydrogen.To increase the faradaic efficiency by optimizing the materials,the cell design,or the operating strategy is a key challenge to address for future developments of PCCEL in order to achieve even more superior techno-economic merits.