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

Chinese Physics B 2024年第3期33卷 291-301页

作者：申瑾赵建东刘华清姜锐余智鑫 School of Systems Science Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China School of Maritime and Transportation Ningbo UniversityNingbo 315000China

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 effi

关键词： heterogeneous traffic flow CAV linear stability nonlinear stability energy consumption

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Impact of COVID-19 on the energy consumption of commercial buildings:A case study in Singapore

Energy and Built Environment

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energy and Built Environment 2024年第3期5卷 364-373页

作者： Senhong Cai Zhonghua Gou School of Urban Design Wuhan UniversityWuhanChina

Numerous studies have demonstrated that commercial activities have significantly reduced during COVID-19,while there are few studies disclosing the consequent impacts on the energy consumption of commercial build-ings.This study explores the changes in energy consumption of different types of commercial buildings in Sin-gapore under the impact of the pandemic,using commercial building energy performance data from 2017 to 2020(n=540).The sampled buildings include 93 hotel buildings,303 office buildings,106 retail buildings,and 38 mixed developments.The analysis mainly used linear regression and paired sample t-test.The results showed that relative to 2019,the mean energy use intensity(EUI)of sampled commercial buildings decreased by 56.77 kWh/m^(2)in the pandemic year(2020),a plunge of 19.9%.The extent to which the EUI of each type of commercial building is affected by the pandemic is found as:mixed development>retail>office>hotel.The study also identi-fied the factors that significantly influenced the EUI of commercial buildings before and during the pandemic.The results of the study complement existing knowledge about the factors influencing energy consumption in com-mercial buildings by considering the impact of the pandemic and furthermore contribute to the improvement of energy management in commercial buildings by providing directions for building energy efficiency approaches.

关键词： Commercial buildings energy consumption energy use intensity Impact of COVID-19 Influential factors

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Joint Optimization of energy consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

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China Communications 2024年第4期21卷 104-119页

作者： Huang Xiaoge Yin Hongbo Cao Bin Wang Yongsheng Chen Qianbin Zhang Jie School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Communication and Information Engineering University of SheffieldUnited Kingdom

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.

关键词： blockchain energy consumption fog computing network Internet of Things latency

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Physical-data Fusion Modeling Method for energy consumption Analysis of Smart Building

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Journal of Modern Power Systems and Clean energy 2022年第2期10卷 482-491页

作者： Xiao Han Chaohai Zhang Yi Tang Yujian Ye the Jiangsu Key Laboratory of New energy Generation and Power Conversion Nanjing University of Aeronautics and AstronauticsNanjing 211106China the School of Electrical Engineering Southeast UniversityNanjing 210096China the Department of Electrical and Electronic Engineering Imperial College LondonLondonU.K.

The energy consumption of buildings accounts for approximately 40%of total energy consumption.An accurate energy consumption analysis of buildings can not only promise significant energy savings but also help estimate the demand response potential more accurately,and consequently brings benefits to the upstream power grid.This paper proposes a novel physical-data fusion modeling(PFM)method for modeling smart buildings that can accurately assess energy consumption.First,a thermal process model of buildings and an electrical load model that focus on building heating,ventilation,and air conditioning(HVAC)systems are presented to analyze the thermal-electrical conversion process of energy consumption of buildings.Second,the PFM method is used to improve the accuracy of the energy consumption analysis model for buildings by modifying the parameters that are difficult to measure in the physical model(i.e.,it effectively modifies the electrical load model based on the proposed PFM method).Finally,case studies involving a real-world dataset recorded in a high-tech park in Changzhou,China,demonstrate that the proposed method exhibits superior performance with respect to the traditional physical modeling(TPM)method and data-driven modeling(DDM)method in terms of the achieved accuracy.

关键词： Smart building physical-data fusion modeling method energy consumption precision model thermal-electrical conversion.

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Application of Internet of Things in the Health Sector:Toward Minimizing energy consumption

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Big Data Mining and Analytics 2022年第4期5卷 302-308页

作者： Mohammed Moutaib Tarik Ahajjam Mohammed Fattah Yousef Farhaoui Badraddine Aghoutane Moulhime El Bekkali IMAGE Laboratory Moulay Ismail UniversityMeknes 50000Morocco Department of Computer Science Faculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco IA Laboratory Faculty of ScienceMoulay Ismail UniversityMeknes 50000Morocco Sidi Mohamed Ben Abdellah University Fes 30000Morocco

The Internet of Things(IoT)is currently reflected in the increase in the number of connected objects,that is,devices with their own identity and computing and communication capacities.IoT is recognized as one of the most critical areas for future technologies,gaining worldwide attention.It applies to many areas,where it has achieved success,such as healthcare,where a patient is monitored using nodes and lightweight sensors.However,the powerful functions of IoT in the medical field are based on communication,analysis,processing,and management of data autonomously without any manual intervention,which presents many difficulties,such as energy consumption.However,these issues significantly slow down the development and rapid deployment of this technology.The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading.The distance between nodes is one of the factors influencing energy consumption.In this article,we have proposed direct communication between nodes to avoid collision domains,which will help reduce data retransmission.The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.

关键词： Internet of Things(IoT) energy consumption cloud computing data storage

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Combination of steam-enhanced extraction and electrical resistance heating for efficient remediation of perchloroethylene-contaminated soil:Coupling merits and energy consumption

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Frontiers of Environmental Science & Engineering 2022年第11期16卷 249-260页

作者： Rui Yue Zhikang Chen Liujun Liu Lipu Yin Yicheng Qiu Xianhui Wang Zhicheng Wang Xuhui Mao School of Resource and Environmental Sciences Wuhan UniversityWuhan 430079China China State Science Dingshi Environmental Engineering Company Beijing 100020China

In situ thermal desorption(ISTD)technology effectively remediates soil contaminated by dense nonaqueous phase liquids(DNAPLs).However,more efforts are required to minimize the energy consumption of ISTD technology.This study developed a laboratory-scale experimental device to explore the coupling merits of two traditional desorption technologies:steam-enhanced extraction(SEE)and electrical resistance heating(ERH).The results showed that injecting high-density steam(>1 g/min)into loam or clay with relatively high moisture content(>13.3%)could fracture the soil matrix and lead to the occurrence of the preferential flow of steam.For ERH alone,the electrical resistance and soil moisture loss were critical factors influencing heating power.When ERH and SEE were combined,preheating soil by ERH could increase soil permeability,effectively alleviating the problem of preferential flow of SEE.Meanwhile,steam injection heated the soil and provided moisture for maintaining soil electrical conductivity,thereby ensuring power stability in the ERH process.Compared with ERH alone(8 V/cm)and SEE alone(1 g/min steam),the energy consumption of combined method in remediating perchloroethylene-contaminated soil was reduced by 39.3%and 52.9%,respectively.These findings indicate that the combined method is more favorable than ERH or SEE alone for remediating DNAPL-contaminated subsurfaces when considering ISTD technology.

关键词： Steam-enhanced extraction Electrical resistance heating Dense nonaqueous phase liquid Soil remediation energy consumption

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Hourly energy consumption characteristics of metro rail transit:Train traction versus station operation

Energy and Built Environment

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energy and Built Environment 2023年第5期4卷 568-575页

作者： Bowen Guan Xiaohua Liu Tao Zhang Xinke Wang School of Human Settlements and Civil Engineering Xi’an Jiaotong UniversityXi’anShaanxi710054China Department of Building Science Tsinghua UniversityBeijing 100084China

The electricity consumption of the urban metro system can be mainly divided into the following two categories:the electricity consumption for train traction(E_(t))and the electricity consumption for station operation(E_(s)).Although understanding the hourly fluctuation characteristics of E_(t) and E_(s) contributes to renewable energy inte-gration and achieving carbon emission reduction of the metro system,the hourly fluctuation characteristics have been poorly reported in the literature.Thus,a typical underground non-transfer metro station of a city’s metro system in the North China Plain is selected in this study,and E_(t) and E_(s) were monitored to portray their hourly fluctuation characteristics.Results reveal that the hourly E_(t) shows a significant intraday“U”shape on weekdays,indicating two symmetric peaks in morning and evening rush hours.While the hourly E_(s) shows an intraday“flat”shape,indicating it is nearly free from the effect of rush hour.Moreover,it is statistically proved that the train frequency is the core influencing factor resulting in the intraday fluctuation of hourly E_(t).In the case study,when the train frequency increases from the mean(20 trains per hour)to maximum(32 trains per hour),the hourly E_(t) will increase by 53.4%.

关键词： Urban rail transit energy consumption Hourly fluctuation Train traction Station operation Carbon emission reduction

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Optimization of Chiller Loading Problem Using Improved Golden Jackal Optimization Algorithm Leads to Reduction in energy consumption

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energy Engineering 2023年第11期120卷 2565-2583页

作者： Na Dong Xiao Yang Nasser Yousefi School of Mechanical Engineering Henan Institute of TechnologyXinxiang453000China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang453000China Karaj Branch Islamic Azad UniversityKaraj***Iran College of Technical Engineering The Islamic UniversityNajaf54001Iraq

This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.

关键词： Optimal chiller loading improved version of golden jackal optimization energy consumption

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Generating Synthetic Data to Reduce Prediction Error of energy consumption

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Computers, Materials & Continua 2022年第2期70卷 3151-3167页

作者： Debapriya Hazra Wafa Shafqat Yung-Cheol Byun Department of Computer Engineering Jeju National UniversityJeju-siKorea

Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions.energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing.Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies.The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data.Another critical factor is balancing the data for enhanced prediction.Data Augmentation is a technique used for increasing the data available for training.Synthetic data are the generation of new data which can be trained to improve the accuracy of prediction models.In this paper,we propose a model that takes time series energy consumption data as input,pre-processes the data,and then uses multiple augmentation techniques and generative adversarial networks to generate synthetic data which when combined with the original data,reduces energy consumption prediction error.We propose TGAN-skip-Improved-WGAN-GP to generate synthetic energy consumption time series tabular data.We modify TGANwith skip connections,then improveWGANGPby defining a consistency term,and finally use the architecture of improved WGAN-GP for training TGAN-skip.We used various evaluation metrics and visual representation to compare the performance of our proposed model.We also measured prediction accuracy along with mean and maximum error generated while predicting with different variations of augmented and synthetic data with original data.The mode collapse problemcould be handled by TGAN-skip-Improved-WGAN-GP model and it also converged faster than existing GAN models for synthetic data generation.The experiment result shows that our proposed technique of combining synthetic data with original data could significantly reduce the prediction error rate and increase the p

关键词： energy consumption generative adversarial networks synthetic data time series data TGAN WGAN-GP TGAN-skip prediction error augmentation

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Intelligent energy consumption For Smart Homes Using Fused Machine-Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

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.

关键词： energy consumption intelligent machine learning technique smart homes prediction

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