In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-p...In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.展开更多
In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assiste...In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.展开更多
The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-ran...The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.展开更多
Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniqu...Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.展开更多
Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance ...Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance optimization of HRVs under cold climatic conditions,where conventional ventilation systems increase heat loss.A comprehensive numerical model was developed using COMSOL Multiphysics,integrating fluid dynamics,heat transfer,and solid mechanics to evaluate the thermal efficiency and structural integrity of an HRV system.The methodology employed a detailed geometry with tetrahedral elements,temperature-dependent material properties,and coupled governing equations solved under Tehran-specific boundary conditions.A multi-objective optimization was implemented in the framework of the Nelder-Mead simplex algorithm,targeting the maximization of the average outlet temperature and minimization of the maximum von Mises thermal stress,with inlet flow velocity as the design variable(range:0.5–1.2m/s).Results indicate an optimal velocity of 0.51563 m/s,achieving an average outlet temperature of 289.44 K and maximum von Mises stress of 221 MPa,validated through mesh independence and detailed contour analyses of temperature,velocity,and stress distributions.展开更多
It’s systematically analyzed that energy efficiency optimization technology has been applied in the field of steel industry. The fundamental principal of energy optimization technology is reasonably matching the qual...It’s systematically analyzed that energy efficiency optimization technology has been applied in the field of steel industry. The fundamental principal of energy optimization technology is reasonably matching the quality and price of energy as well as energy-dominated systematic energy efficiency management system. Specific measures of energy optimization have been put forward, which include taking high efficiency utilized technology such as energy saving from the original, the production process and recycling of waste heat and waste energy etc., integrating and configuring energy in an optimized way of high efficiency and excellent quality, fully realizing the function of different energy in order to optimize the utilization sequence of energy, and improving the energy medium system by themselves. Finally it is clearly pointed out that the steel industry should pay more consideration about the great deal of energy system which they have used now and an ideal energy evaluation methodology and standard should be built as soon as possible if they want to take full usage of the real role and function of energy in all aspects.展开更多
A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal...A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.展开更多
In recent days,internet of things is widely implemented in Wireless Sensor Network(WSN).It comprises of sensor hubs associated together through the WSNs.The WSNis generally affected by the power in battery due to the ...In recent days,internet of things is widely implemented in Wireless Sensor Network(WSN).It comprises of sensor hubs associated together through the WSNs.The WSNis generally affected by the power in battery due to the linked sensor nodes.In order to extend the lifespan of WSN,clustering techniques are used for the improvement of energy consumption.Clustering methods divide the nodes in WSN and form a cluster.Moreover,it consists of unique Cluster Head(CH)in each cluster.In the existing system,Soft-K means clustering techniques are used in energy consumption in WSN.The soft-k means algorithm does not work with the large-scale wireless sensor networks,therefore it causes reliability and energy consumption problems.To overcome this,the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic(LBC-COFL)algorithm is used.The main objective is to perform the lifespan by balancing the gateways with the load of less energy.The proposed algorithm is evaluated using the metrics such as energy consumption,throughput,central tendency,network lifespan,and total energy utilization.展开更多
With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid elec...With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.展开更多
The expansion of renewable energy sources(RESs)in European Union countries has given rise to the development of Renewable Energy Communities(RECs),which aremade up of locally generated energy by these RESs controlled ...The expansion of renewable energy sources(RESs)in European Union countries has given rise to the development of Renewable Energy Communities(RECs),which aremade up of locally generated energy by these RESs controlled by individuals,businesses,enterprises,and public administrations.There are several advantages for creating these RECs and participating in them,which include social,environmental,and financial.Nonetheless,according to the Renewable Energy Directive(RED II),the idea of RECs has given opportunities for researchers to investigate the behavior from all aspects.These RECs are characterized by energy fluxes corresponding to self-consumption,energy sales,and energy sharing.Our work focuses on amathematical time-dependentmodel on an hourly basis that considers the optimization of photovoltaic-based RECs tomaximize profit based on the number of prosumers and consumers,as well as the impact of load profiles on the community’s technical and financial aspects usingMATLAB software.In this work,REC’s users can install their plant and become prosumers or vice versa,and users could change their consumption habits until the optimumconfiguration of REC is obtained.Moreover,this work also focuses on the financial analysis of the plant by comparing the Net Present Value(NPV)as a function of plant size,highlighting the advantage of creating a REC.Numerical results have been obtained investigating the case studies of RECs as per the Italian framework,which shows an optimal distribution of prosumers and consumers and an optimal load profile in which the maximum profitability is obtained.Optimization has been performed by considering different load profiles.Moreover,starting from the optimized configurations,an analysis based on the plant size is also made to maximize the NPV.This work has shown positive outcomes and would be helpful for the researchers and stakeholders while designing the RECs.展开更多
Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become...Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become a significant instrument for propelling the energy revolution and ensuring energy security.Under increasingly stringent carbon emission constraints,how to achieve multi-dimensional improvements in energy utilization efficiency,renewable energy accommodation levels,and system economics-through the intelligent coupling of diverse energy carriers such as electricity,heat,natural gas,and hydrogen,and the effective application of market-based instruments like carbon trading and demand response-constitutes a critical scientific and engineering challenge demanding urgent solutions.展开更多
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ...Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.展开更多
A two-level layout optimization strategy is proposed in this paper for large-scale composite wing structures. Design requirements are adjusted at the system level according to structural deformation, while the layout ...A two-level layout optimization strategy is proposed in this paper for large-scale composite wing structures. Design requirements are adjusted at the system level according to structural deformation, while the layout is optimized at the subsystem level to satisfy the constraints from system level. The approaching degrees of various failure critical loads in wing panels are employed to gauge the structure’s carrying efficiency. By optimizing the efficiency as an objective, the continuity of the problem could be guaranteed. Stiffened wing panels are modeled by the equivalent orthotropic plates, and the global buckling load is predicted by energy method. The nonlinear effect of stringers’ support elasticity on skin local buckle resistance is investigated and approximated by neural network （NN） surrogate model. These failure predictions are based on analytical solutions, which could effectively save calculation resources. Finally, the integral optimization of a large-scale wing structure is completed as an example. The result fulfills design requirements and shows the feasibility of this method.展开更多
The space-air-ground integrated network(SAGIN)has gained widespread attention from academia and industry in recent years.It is widely applied in many practical fields such as global observation and mapping,intelligent...The space-air-ground integrated network(SAGIN)has gained widespread attention from academia and industry in recent years.It is widely applied in many practical fields such as global observation and mapping,intelligent transportation systems,and military missions.As an information carrier of air platforms,the deployment strategy of unmanned aerial vehicles(UAVs)is essential for communication systems’performance.In this paper,we discuss a UAV broadcast coverage strategy that can maximize energy efficiency(EE)under terrestrial users’requirements.Due to the non-convexity of this issue,conventional approaches often solve with heuristics algorithms or alternate optimization.To this end,we propose an iterative algorithm by optimizing trajectory and power allocation jointly.Firstly,we discrete the UAV trajectory into several stop points and propose a user grouping strategy based on the traveling salesman problem(TSP)to acquire the number of stop points and the optimization range.Then,we use the Dinkelbach method to dispose of the fractional form and transform the original problem into an iteratively solvable convex optimization problem by variable substitution and Taylor approximation.Numerical results validate our proposed solution and outperform the benchmark schemes in EE and mission completion time.展开更多
Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained systems.The system gains are usually optimized by designing resource all...Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained systems.The system gains are usually optimized by designing resource allocation algorithm appropriately.However,few works focus on the interaction that channel’s time-vary characters make the energy transfer inefficiently.To address this,we propose a novel system operation sequence for sensor-cloud system where the Sinks provide SWIPT for sensor nodes opportunistically during downlink phase and collect the data transmitted from sensor nodes in uplink phase.Then,the energy-efficiency maximization problem of the Sinks is presented by considering the time costs and energy consumption of channel detection.It is proved that the formulated problem is an optimal stopping process with optimal stopping rules.An optimal energy-efficiency(OEE)algorithm is designed to obtain the optimal stopping rules for SWIPT.Finally,the simulations are performed based on the OEE algorithm compared with the other two strategies to verify the effectiveness and gains in improving the system efficiency.展开更多
This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base st...This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base station(BS)adapts the number of antennas to the daily load profile(DLP).This paper takes into consideration user location distribution(ULD)variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system.ULD variation is modeled by dividing the cell into two coverage areas with different user densities:boundary focused(BF)and center focused(CF)ULD.All cells are assumed identical in terms of BS configurations,cell loading,and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load.Together with energy efficiency(EE)we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size,available bandwidth,output power level of the BS,and maximum output power of the power amplifier(PA)at different cell loading.We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business,residential,street,and highway areas.展开更多
Efficient Air Conditioning (A/C) system is the key to reducing energy consumption in building operation. In order to decrease the energy consumption in an A/C system, a method to calculate the optimal tube row number ...Efficient Air Conditioning (A/C) system is the key to reducing energy consumption in building operation. In order to decrease the energy consumption in an A/C system, a method to calculate the optimal tube row number of a direct expansion (DX) cooling coil for minimizing the entropy generation in the DX cooling which functioned as evaporator in the A/C system was developed. The optimal tube row numbers were determined based on the entropy generation minimization (EGM) approach. Parametric studies were conducted to demonstrate the application of the analytical calculation method. Optimal tube row number for different air mass flow rates, inlet air temperatures and sensible cooling loads were investigated. It was found that the optimal tube row number of a DX cooling coil was in the range of 5 - 9 under normal operating conditions. The optimal tube row number was less when the mass flow rate and inlet air temperature were increased. The tube row number increased when the sensible cooling load was increased. The exergy loss when using a non-optimal and optimal tube row numbers was compared to show the advantage of using the optimal tube row number. The decrease of exery loss ranged from around 24% to 70%. Therefore the new analytical method developed in this paper offers a good practice guide for the design of DX cooling coils for energy conservation.展开更多
In WSNs, energy conservation is the primary goal, while throughput and delay are less important. This re-sults in a tradeoff between performance (e.g., throughput, delay, jitter, and packet-loss-rate) and energy con-s...In WSNs, energy conservation is the primary goal, while throughput and delay are less important. This re-sults in a tradeoff between performance (e.g., throughput, delay, jitter, and packet-loss-rate) and energy con-sumption. In this paper, the problem of energy-efficient MAC protocols in WSNs is modeled as a game-theoretic constraint optimization with multiple objectives. After introducing incompletely cooperative game theory, based on the estimated game state (e.g., the number of competing nodes), each node independ-ently implements the optimal equilibrium strategy under the given constraints (e.g., the used energy and QoS requirements). Moreover, a simplified game-theoretic constraint optimization scheme (G-ConOpt) is pre-sented in this paper, which is easy to be implemented in current WSNs. Simulation results show that G-ConOpt can increase system performance while still maintaining reasonable energy consumption.展开更多
Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biolo...Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.展开更多
文摘In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.
基金supported in part by the National Natural Science Foundation of China under Grant 62071253,Grant 62371252 and Grant 62271268in part by the Jiangsu Provincial Key Research and Development Program under Grant BE2022800in part by the Jiangsu Provincial 333 Talent Project.
文摘In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.
基金funded by King Saud University Researchers Supporting Project Number(RSPD2025R1007),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.
文摘Water power is one of the key renewable energy resources,whose efficiency is often hampered due to inefficient water flow management,turbine performance,and environmental variations.Most existing optimization techniques lack the real-time adaptability to sufficiently allocate resources in terms of location and time.Hence,a novel Scalable Tas-manian Devil Optimization(STDO)algorithm is introduced to optimize hydropower generation for maximum power efficiency.Using the STDO to model important system characteristics including water flow,turbine changes,and energy conversion efficiency is part of the process.In the final analysis,optimizing these settings in would help reduce inefficiencies and maximize power generation output.Following that,simulations based on actual hydroelectric data are used to analyze the algorithm's effectiveness.The simulation results provide evidence that the STDO algorithm can enhance hydropower plant efficiency tremendously translating to considerable energy output augmentation compared to conven-tional optimization methods.STDO achieves the reliability(92.5),resiliency(74.3),and reduced vulnerability(9.3).To guarantee increased efficiency towards ecologically friendly power generation,the STDO algorithm may thus offer efficient resource optimization for hydropower.A clear route is made available for expanding the efficiency of current hydropower facilities while tackling the long-term objectives of reducing the environmental impact and increasing the energy output of energy produced from renewable sources.
文摘Heat Recovery Ventilators(HRVs)are essential for improving indoor air quality(IAQ)and reducing energy consumption in residential buildings situated in cold climates.This study considers the efficiency and performance optimization of HRVs under cold climatic conditions,where conventional ventilation systems increase heat loss.A comprehensive numerical model was developed using COMSOL Multiphysics,integrating fluid dynamics,heat transfer,and solid mechanics to evaluate the thermal efficiency and structural integrity of an HRV system.The methodology employed a detailed geometry with tetrahedral elements,temperature-dependent material properties,and coupled governing equations solved under Tehran-specific boundary conditions.A multi-objective optimization was implemented in the framework of the Nelder-Mead simplex algorithm,targeting the maximization of the average outlet temperature and minimization of the maximum von Mises thermal stress,with inlet flow velocity as the design variable(range:0.5–1.2m/s).Results indicate an optimal velocity of 0.51563 m/s,achieving an average outlet temperature of 289.44 K and maximum von Mises stress of 221 MPa,validated through mesh independence and detailed contour analyses of temperature,velocity,and stress distributions.
文摘It’s systematically analyzed that energy efficiency optimization technology has been applied in the field of steel industry. The fundamental principal of energy optimization technology is reasonably matching the quality and price of energy as well as energy-dominated systematic energy efficiency management system. Specific measures of energy optimization have been put forward, which include taking high efficiency utilized technology such as energy saving from the original, the production process and recycling of waste heat and waste energy etc., integrating and configuring energy in an optimized way of high efficiency and excellent quality, fully realizing the function of different energy in order to optimize the utilization sequence of energy, and improving the energy medium system by themselves. Finally it is clearly pointed out that the steel industry should pay more consideration about the great deal of energy system which they have used now and an ideal energy evaluation methodology and standard should be built as soon as possible if they want to take full usage of the real role and function of energy in all aspects.
基金Supported by the National Science and Technology Support Program(2013BAG12B01)Foundational and Advanced Research Program General Project of Chongqing City(cstc2013jcyjjq60002)
文摘A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 1/282/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R203),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In recent days,internet of things is widely implemented in Wireless Sensor Network(WSN).It comprises of sensor hubs associated together through the WSNs.The WSNis generally affected by the power in battery due to the linked sensor nodes.In order to extend the lifespan of WSN,clustering techniques are used for the improvement of energy consumption.Clustering methods divide the nodes in WSN and form a cluster.Moreover,it consists of unique Cluster Head(CH)in each cluster.In the existing system,Soft-K means clustering techniques are used in energy consumption in WSN.The soft-k means algorithm does not work with the large-scale wireless sensor networks,therefore it causes reliability and energy consumption problems.To overcome this,the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic(LBC-COFL)algorithm is used.The main objective is to perform the lifespan by balancing the gateways with the load of less energy.The proposed algorithm is evaluated using the metrics such as energy consumption,throughput,central tendency,network lifespan,and total energy utilization.
文摘With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.
文摘The expansion of renewable energy sources(RESs)in European Union countries has given rise to the development of Renewable Energy Communities(RECs),which aremade up of locally generated energy by these RESs controlled by individuals,businesses,enterprises,and public administrations.There are several advantages for creating these RECs and participating in them,which include social,environmental,and financial.Nonetheless,according to the Renewable Energy Directive(RED II),the idea of RECs has given opportunities for researchers to investigate the behavior from all aspects.These RECs are characterized by energy fluxes corresponding to self-consumption,energy sales,and energy sharing.Our work focuses on amathematical time-dependentmodel on an hourly basis that considers the optimization of photovoltaic-based RECs tomaximize profit based on the number of prosumers and consumers,as well as the impact of load profiles on the community’s technical and financial aspects usingMATLAB software.In this work,REC’s users can install their plant and become prosumers or vice versa,and users could change their consumption habits until the optimumconfiguration of REC is obtained.Moreover,this work also focuses on the financial analysis of the plant by comparing the Net Present Value(NPV)as a function of plant size,highlighting the advantage of creating a REC.Numerical results have been obtained investigating the case studies of RECs as per the Italian framework,which shows an optimal distribution of prosumers and consumers and an optimal load profile in which the maximum profitability is obtained.Optimization has been performed by considering different load profiles.Moreover,starting from the optimized configurations,an analysis based on the plant size is also made to maximize the NPV.This work has shown positive outcomes and would be helpful for the researchers and stakeholders while designing the RECs.
文摘Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become a significant instrument for propelling the energy revolution and ensuring energy security.Under increasingly stringent carbon emission constraints,how to achieve multi-dimensional improvements in energy utilization efficiency,renewable energy accommodation levels,and system economics-through the intelligent coupling of diverse energy carriers such as electricity,heat,natural gas,and hydrogen,and the effective application of market-based instruments like carbon trading and demand response-constitutes a critical scientific and engineering challenge demanding urgent solutions.
基金funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region:No.22D01B148Bidding Topics for the Center for Integration of Education and Production and Development of New Business in 2024:No.2024-KYJD05+1 种基金Basic Scientific Research Business Fee Project of Colleges and Universities in Autonomous Region:No.XJEDU2025P126Xinjiang College of Science&Technology School-level Scientific Research Fund Project:No.2024-KYTD01.
文摘Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.
基金National Natural Science Foundation of China （10872091）
文摘A two-level layout optimization strategy is proposed in this paper for large-scale composite wing structures. Design requirements are adjusted at the system level according to structural deformation, while the layout is optimized at the subsystem level to satisfy the constraints from system level. The approaching degrees of various failure critical loads in wing panels are employed to gauge the structure’s carrying efficiency. By optimizing the efficiency as an objective, the continuity of the problem could be guaranteed. Stiffened wing panels are modeled by the equivalent orthotropic plates, and the global buckling load is predicted by energy method. The nonlinear effect of stringers’ support elasticity on skin local buckle resistance is investigated and approximated by neural network （NN） surrogate model. These failure predictions are based on analytical solutions, which could effectively save calculation resources. Finally, the integral optimization of a large-scale wing structure is completed as an example. The result fulfills design requirements and shows the feasibility of this method.
基金co-supported by National Natural Science Foundation of China (No. 62171158)the Major Key Project of PCL (PCL2021A03-1)
文摘The space-air-ground integrated network(SAGIN)has gained widespread attention from academia and industry in recent years.It is widely applied in many practical fields such as global observation and mapping,intelligent transportation systems,and military missions.As an information carrier of air platforms,the deployment strategy of unmanned aerial vehicles(UAVs)is essential for communication systems’performance.In this paper,we discuss a UAV broadcast coverage strategy that can maximize energy efficiency(EE)under terrestrial users’requirements.Due to the non-convexity of this issue,conventional approaches often solve with heuristics algorithms or alternate optimization.To this end,we propose an iterative algorithm by optimizing trajectory and power allocation jointly.Firstly,we discrete the UAV trajectory into several stop points and propose a user grouping strategy based on the traveling salesman problem(TSP)to acquire the number of stop points and the optimization range.Then,we use the Dinkelbach method to dispose of the fractional form and transform the original problem into an iteratively solvable convex optimization problem by variable substitution and Taylor approximation.Numerical results validate our proposed solution and outperform the benchmark schemes in EE and mission completion time.
基金This work was supported by Scientific Research Ability Improving Foundation for Young and Middle-Aged University Teachers in Guangxi(No.2020KY04030)The school introduces talents to start scientific research projects(No.2019KJQD17)+1 种基金This work was supported in part by the National Natural Science Foundation of China(No.61762010,No.61862007)Guangxi Natural Science Foundation(No.2018GXNSFAA138147).
文摘Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained systems.The system gains are usually optimized by designing resource allocation algorithm appropriately.However,few works focus on the interaction that channel’s time-vary characters make the energy transfer inefficiently.To address this,we propose a novel system operation sequence for sensor-cloud system where the Sinks provide SWIPT for sensor nodes opportunistically during downlink phase and collect the data transmitted from sensor nodes in uplink phase.Then,the energy-efficiency maximization problem of the Sinks is presented by considering the time costs and energy consumption of channel detection.It is proved that the formulated problem is an optimal stopping process with optimal stopping rules.An optimal energy-efficiency(OEE)algorithm is designed to obtain the optimal stopping rules for SWIPT.Finally,the simulations are performed based on the OEE algorithm compared with the other two strategies to verify the effectiveness and gains in improving the system efficiency.
文摘This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base station(BS)adapts the number of antennas to the daily load profile(DLP).This paper takes into consideration user location distribution(ULD)variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system.ULD variation is modeled by dividing the cell into two coverage areas with different user densities:boundary focused(BF)and center focused(CF)ULD.All cells are assumed identical in terms of BS configurations,cell loading,and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load.Together with energy efficiency(EE)we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size,available bandwidth,output power level of the BS,and maximum output power of the power amplifier(PA)at different cell loading.We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business,residential,street,and highway areas.
文摘Efficient Air Conditioning (A/C) system is the key to reducing energy consumption in building operation. In order to decrease the energy consumption in an A/C system, a method to calculate the optimal tube row number of a direct expansion (DX) cooling coil for minimizing the entropy generation in the DX cooling which functioned as evaporator in the A/C system was developed. The optimal tube row numbers were determined based on the entropy generation minimization (EGM) approach. Parametric studies were conducted to demonstrate the application of the analytical calculation method. Optimal tube row number for different air mass flow rates, inlet air temperatures and sensible cooling loads were investigated. It was found that the optimal tube row number of a DX cooling coil was in the range of 5 - 9 under normal operating conditions. The optimal tube row number was less when the mass flow rate and inlet air temperature were increased. The tube row number increased when the sensible cooling load was increased. The exergy loss when using a non-optimal and optimal tube row numbers was compared to show the advantage of using the optimal tube row number. The decrease of exery loss ranged from around 24% to 70%. Therefore the new analytical method developed in this paper offers a good practice guide for the design of DX cooling coils for energy conservation.
文摘In WSNs, energy conservation is the primary goal, while throughput and delay are less important. This re-sults in a tradeoff between performance (e.g., throughput, delay, jitter, and packet-loss-rate) and energy con-sumption. In this paper, the problem of energy-efficient MAC protocols in WSNs is modeled as a game-theoretic constraint optimization with multiple objectives. After introducing incompletely cooperative game theory, based on the estimated game state (e.g., the number of competing nodes), each node independ-ently implements the optimal equilibrium strategy under the given constraints (e.g., the used energy and QoS requirements). Moreover, a simplified game-theoretic constraint optimization scheme (G-ConOpt) is pre-sented in this paper, which is easy to be implemented in current WSNs. Simulation results show that G-ConOpt can increase system performance while still maintaining reasonable energy consumption.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11105062 and 11265014the Fundamental Research Funds for the Central Universities under Grant Nos LZUJBKY-2011-57 and LZUJBKY-2015-119
文摘Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.
基金supported by Major International(Regional)Joint Research Project of the National Natural Science Foundation of China(61320106011)National High Technology Research and Development Program of China(863 Program)(2014AA052802)National Natural Science Foundation of China(61573224)
