Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.

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.

Exploring the impact of economic growth and energy consumption on SO_(2) emissions in China based on the Environmental Kuznets Curve hypothesis

This study aims to analysis the influence of economic growth(EG)and energy consumption(EC)on sulfur dioxide emissions(SE)in China.Accordingly,this study explores the link between EG,EC,and SE for 30 provinces in China over the span of 2000-2019.This study also analyzes cross-sectional dependence tests,panel unit root tests,Westerlund panel cointegration tests,Dumitrescu-Hurlin(D-H)causality tests.According to the test results,there is an inverted U-shaped association between EG and SE,and the assumption of the Environmental Kuznets Curve(EKC)is verified.The signs of EG and EC in the fixed effect(FE)and random effect(RE)methods are in line with those in the dynamic ordinary least squares(DOLS),fully modified ordinary least squares(FMOLS)and autoregressive distributed lag(ARDL)estimators.Moreover,the results verified that EC can obviously positive impact the SE.To reduce SE in China,government and policymakers can improve air quality by developing cleaner energy sources and improving energy efficiency.This requires the comprehensive use of policies,regulations,economic incentives,and public participation to promote sustainable development.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

NeTrainSim:a network-level simulator for modeling freight train longitudinal motion and energy consumption

Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the Ne Train Sim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator's performance. The first case study validates the model by comparing Ne Train Sim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safefollowing distances between successive trains. The next study highlights the simulator's ability to resolve train conflicts for different scenarios. Finally, the suitability of the Ne Train Sim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

Optimising Energy Consumption in SD-DCN Networks (Software Defined-Data Center Network)

Over the last decade, the rapid growth in traffic and the number of network devices has implicitly led to an increase in network energy consumption. In this context, a new paradigm has emerged, Software-Defined Networking (SDN), which is an emerging technique that separates the control plane and the data plane of the deployed network, enabling centralized control of the network, while offering flexibility in data center network management. Some research work is moving in the direction of optimizing the energy consumption of SD-DCN, but still does not guarantee good performance and quality of service for SDN networks. To solve this problem, we propose a new mathematical model based on the principle of combinatorial optimization to dynamically solve the problem of activating and deactivating switches and unused links that consume energy in SDN networks while guaranteeing quality of service (QoS) and ensuring load balancing in the network.

Energy Consumption Analysis and Optimization of Electric Submersible Pump System

Electric submersible pumps account for a considerable proportion in the development of the Bohai Oilfield. Improving the system efficiency of the electric submersible pump wells, ensuring that the units operate in the high-efficiency zone, is essential. Analysis shows that the efficiency of the electric submersible pump system depends on the wear and tear of each component of the submersible pump equipment, the setting of operational parameters, and more importantly, the production status and daily management level of the oil well. Therefore, improving the structural performance of the submersible pump product, optimizing the parameters setting of the oil well, strengthening daily management, establishing a scientific management system, and improving the production management process and system can effectively improve the production efficiency and economic benefits of the oil well, and further achieve the goal of energy saving and emission reduction. In addition, it is necessary to actively promote the concept and technology of energy saving and emission reduction, encourage oilfield enterprises to explore effective measures to reduce the energy consumption of the electric submersible pump system by strengthening the scientific management system, and achieve a green, low-carbon, and high-quality development of oilfield production to achieve the unity of economic benefits, social benefits, and environmental benefits. This article applies the above measures in the P oilfield to achieve energy optimization of submersible electric pump systems, reducing the daily power consumption of single well submersible electric pump systems by 371 kWh per day, increasing the submersible electric pump's lifespan by 200 days, generating considerable project benefits.

Thermal comfort assessment and energy consumption analysis of ground-source heat pump system combined with radiant heating/cooling

A new ground source heat pump system combined with radiant heating/cooling is proposed, and the principles and the advantages of the system are analyzed. A demonstration of the system is applied to a rebuilt building： Xijindu exhibition hall, which is located in Zhenjiang city in China. Numerical studies on the thermal comfort and energy consumption of the system are carded out by using TRNSYS software. The results indicate that the system with the radiant floor method or the radiant ceiling method shows good thermal comfort without mechanical ventilation in winter. However, the system with either of the methods should add mechanical ventilation to ensure good comfort in summer. At the same level of thermal comfort, it can also be found that the annual energy consumption of the radiant ceiling system is less than that of the radiant floor system.

Distributed Multi-hop Clustering Approach with Low Energy Consumption in WSN

The purpose of sensing the environment and geographical positions,device monitoring,and information gathering are accomplished using Wireless Sensor Network(WSN),which is a non-dependent device consisting of a distinct collection of Sensor Node(SN).Thus,a clustering based on Energy Efficient(EE),one of the most crucial processes performed in WSN with distinct environments,is utilized.In order to efficiently manage energy allocation during sensing and communication,the present research on managing energy efficiency is performed on the basis of distributed algorithm.Multiples of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of the focus of EE methods on energy harvesting and minimum Energy Consumption(EC).The three stages of performance are proposed in this research work.At the outset,during routing and Route Searching Time(RST)with fluctuating node density and PKTs,EC is reduced by the Hybrid Energy-based Multi-User Routing(HEMUR)model proposed in this work.Energy efficiency and an ideal route for various SNs with distinct PKTs in WSN are obtained by this model.By utilizing the Approximation Algorithm(AA),the Bregman Tensor Approximation Clustering(BTAC)is applied to improve the Route Path Selection(RPS)efficiency for Data Packet Transmission(DPT)at the Sink Node(SkN).The enhanced Network Throughput Rate(NTR)and low DPT Delay are provided by BTAC.To MAX the Clustering Efficiency(CE)and minimize the EC,the Energy Effective Distributed Multi-hop Clustering(GISEDC)method based on Generalized Iterative Scaling is implemented.The Multi-User Routing(MUR)is used by the HEMUR model to enhance the EC by 20%during routing.When compared with other advanced techniques,the Average Energy Per Packet(AEPP)is enhanced by 39%with the application of proportional fairness with Boltzmann Distribution(BD).The Gaussian Fast Linear Combinations(GFLC)with AA are applied by BTAC method with an enhanced Communication Overhead(COH)for an increase in performance by 19%and minimize the DPT delay by 23%.When compared with the rest of the advanced techniques,CE is enhanced by 8%and EC by 27%with the application of GISEDC method.

Comparative Study on Energy Consumption Structure of Ordinary Rural Households and Rural Tourism Households in Zhejiang Province, China

With the rapid development of rural tourism in China, more and more rural households operate a rural tourism business. The purpose of this study is to understand the energy consumption characteristic of ordinary rural households (ORHs) and rural tourism households (RTHs) in the mountainous area and islands area in Zhejiang province. 225 households were surveyed, including 185 ORHs and 40 RTHs, based on a field survey in Quzhou (mountainous area) and Zhoushan (islands area). Results reveal that energy consumption of ORHs is low, but energy comsumption of RTHs is high, about 3 to 5 times higher than that of ORHs. Given the results, the government and RTHs should pay more attention to take measures to reduce energy comsumption. Meanwhile, the factors affecting households’ energy consumption are also analyzed. Energy consumption of ORHs is affected by frequently used area, family income level and permanent population. Then energy consumption of RTHs is mainly related to the total building area, number of air conditioner (AC), number of guestrooms and family income level.

Economic feasibility and efficiency enhancement approaches for in situ upgrading of low-maturity organic-rich shale from an energy consumption ratio perspective

The technical feasibility of in situ upgrading technology to develop the enormous oil and gas resource potential in low-maturity shale is widely acknowledged.However,because of the large quantities of energy required to heat shale,its economic feasibility is still a matter of debate and has yet to be convincingly demonstrated quantitatively.Based on the energy conservation law,the energy acquisition of oil and gas generation and the energy consumption of organic matter cracking,shale heat-absorption,and surrounding rock heat dissipation during in situ heating were evaluated in this study.The energy consumption ratios for different conditions were determined,and the factors that influence them were analyzed.The results show that the energy consumption ratio increases rapidly with increasing total organic carbon(TOC)content.For oil-prone shales,the TOC content corresponding to an energy consumption ratio of 3 is approximately 4.2%.This indicates that shale with a high TOC content can be expected to reduce the project cost through large-scale operation,making the energy consumption ratio after consideration of the project cost greater than 1.In situ heating and upgrading technology can achieve economic benefits.The main methods for improving the economic feasibility by analyzing factors that influence the energy consumption ratio include the following:(1)exploring technologies that efficiently heat shale but reduce the heat dissipation of surrounding rocks,(2)exploring technologies for efficient transformation of organic matter into oil and gas,i.e.,exploring technologies with catalytic effects,or the capability to reduce in situ heating time,and(3)establishing a horizontal well deployment technology that comprehensively considers the energy consumption ratio,time cost,and engineering cost.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

Research on the energy consumption mechanism and characteristics of the gallium indium tin liquid metal arcing process

For high-voltage direct current(HVDC)power grid transmission with higher voltages,the energyconsuming branch of the DC circuit breaker is required to dissipate huge energies of more than megajoules in a short time in the case of a fault and short circuit.The requirements for huge volume and weight are difficult to meet with energy-consuming equipment based on ZnO.In this paper,a new energy consumption method is proposed based on gallium indium tin(GaInSn)liquid metal in the arcing process,and a test platform with adjustable short-circuit current is built.The mechanism triggering GaInSn liquid metal arcing energy consumption is studied.It is found that short-circuit current and channel aperture are the key parameters affecting the energy consumption of liquid metal arcing.The characteristics of GaInSn liquid metal energy consumption are investigated,and four stages of liquid metal energy consumption are found:oscillatory shrinkage,arc breakdown,arc burning phase change and arc extinction.The influence of short-circuit current and channel aperture on the energy consumption characteristics of GaInSn liquid metal is investigated.To further explore the physical mechanism of the above phenomena,a magneto-hydrodynamic model of energy consumption in the GaInSn liquid metal arcing process is established.The influence of short-circuit current and channel aperture on the temperature distribution of the liquid metal arc is analyzed.The mechanism of the effect of short-circuit current and channel aperture on peak arc temperature and the temperature diffusion rate is clarified.The research results provide theoretical support for this new liquid metal energy consumption mode DC circuit breaker.

Scheduling an Energy-Aware Parallel Machine System with Deteriorating and Learning Effects Considering Multiple Optimization Objectives and Stochastic Processing Time

Currently,energy conservation draws wide attention in industrial manufacturing systems.In recent years,many studies have aimed at saving energy consumption in the process of manufacturing and scheduling is regarded as an effective approach.This paper puts forwards a multi-objective stochastic parallel machine scheduling problem with the consideration of deteriorating and learning effects.In it,the real processing time of jobs is calculated by using their processing speed and normal processing time.To describe this problem in a mathematical way,amultiobjective stochastic programming model aiming at realizing makespan and energy consumption minimization is formulated.Furthermore,we develop a multi-objective multi-verse optimization combined with a stochastic simulation method to deal with it.In this approach,the multi-verse optimization is adopted to find favorable solutions from the huge solution domain,while the stochastic simulation method is employed to assess them.By conducting comparison experiments on test problems,it can be verified that the developed approach has better performance in coping with the considered problem,compared to two classic multi-objective evolutionary algorithms.

Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

Most studies have conducted experiments on predicting energy consumption by integrating data formodel training.However, the process of centralizing data can cause problems of data leakage.Meanwhile,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework.However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te model.Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

Does economic growth stimulate energy consumption?New evidence from national and regional levels in China

A detailed investigation of the nexus between economic growth and energy use is imperative for formulating sustainable development policies.In this study,we examine panel cointegration and causality relations among economic growth,energy use,capital stock,and labor in 30 Chinese provinces between 2000-2019.We conduct a comprehensive empirical analysis based on panel modeling and a neoclassical production function.The findings of the second-generation panel unit root and co-integration tests reveal that these variables have long term co-integration linkages.We then perform a panel cointegration estimation using the fully modified ordinary least squares technique and find that total energy consumption,electricity consumption,capital stock,and labor significantly influence economic growth at the national and regional levels in China.Moreover,the outcomes of the Dumitrescu-Hurlin causality test indicate the existence of a two-way causal nexus between economic output and total energy consumption at the national level,but only a causal link from GDP to total energy use in the eastern and central regions.Conversely,a causality from total energy use to economic output is identified in the western region.Finally,we provide policy implications for the sustainable development of both energy and the economy at the national and regional levels.

Energy consumption of electrooxidation systems with boron-doped diamond electrodes in the pulse current mode

A pulse current technique was conducted in a boron-doped diamond （BDD） anode system for electrochemical waste- water treatment. Due to the strong generation and weak absorption of hydroxyl radicals on the diamond surface, the BDD elec- trode possesses a powerful capability of electrochemical oxidation of organic compounds, especially in the pulse current mode. The influences of pulse current parameters such as current density, pulse duty cycle, and frequency were investigated in terms of chemical oxygen demand （COD） removal, average current efficiency, and specific energy consumption. The results demon- strated that the relatively high COD removal and low specific energy consumption were obtained simultaneously only if the current density or pulse duty cycle was adjusted to a reasonable value. Increasing the frequency slightly enhanced the COD re- moval and average current efficiency. A pulse-BDD anode system showed a stronger energy saving ability than a constant-BDD anode system when the electrochemical oxidation of phenol of the two systems was compared. The results prove that the pulse current technique is more cost-effective and more suitable for a BDD anode system for real wastewater treatment. A kinetic analysis was presented to explain the above results.

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.

An IoT-Based Energy Conservation Smart Classroom System

With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an influential role in energy conservation such as the Internet of Things(IoT).In this article,we describe the design and implementation of an IoT-based energy conser-vation smart classroom system that contributes to energy conservation in the edu-cation domain.The proposed system not only allows the user to access and control IoT devices(e.g.,lights,projectors,and air conditions)in real-time,it also has the capability to aggregate the estimated energy consumption of an IoT device,the smart classroom,and the building based on the energy consumption and cost model that we propose.Moreover,the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company(SEC)rates.Furthermore,the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't used.The feasibility and benefits of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering,Taibah University,Yanbu branch.The results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.