Energy Efficiency Performance Research Articles

Optimizing Internet-Wide Port Scanning for IoT Security and Network Resilience: A Reinforcement Learning-Based Approach in WLANs with IEEE 802.11ah

ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has spurred the need for robust security mechanisms within wireless local area network (WLAN) environments. Specifically, the IEEE 802.11ah (WiFi-HaLow) technology offers low-power, long-range communication capabilities ideally suited for IoT devices, but these devices face security threats due to constrained computational resources. This research addresses the challenge of optimizing Internet-wide port scanning to enhance IoT security while minimizing disruptions to network performance. In this paper, we propose a novel reinforcement learning-based approach to achieve this optimization. Our approach harnesses the power of the Proximal Policy Optimization (PPO) algorithm to guide decision-making for IoT security and network performance enhancement. The proposed solution entails training an agent to dynamically adjust the port scanning rate, ensuring the delicate balance between security improvement and network responsiveness. We establish a comprehensive system model, encompassing WiFi-HaLow infrastructure, IPv6-enabled IoT devices, and the integration of Internet-security (IPSec) protocols. Mathematical formulations encapsulate constraints such as resource limitations and security-performance trade-offs. Our experimental setup utilizes a carefully curated dataset and a simulation environment to rigorously evaluate the proposed solution’s effectiveness. The proposed approach demonstrates remarkable security enhancement through the prevention of unauthorized access attempts, data breaches, and improved intrusion detection. Moreover, network performance is optimized, with reductions in latency, improvements in throughput, and energy-efficient operation. By presenting a plethora of comparison scenarios, we validate the superiority of our solution against various benchmarks. This research contributes a comprehensive framework that not only advances IoT security within WLAN environments but also highlights the intricate relationship between security and network performance optimization.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

LENet: Lightweight and Effective Detector for Aerial Object

Aerial object detection is crucial in various computer vision tasks, including video monitoring, early warning systems, and visual tracking. While current methods can accurately detect normal-sized objects, they face challenges distinguishing small objects from cluttered backgrounds. Developing methods that can be deployed on edge devices to achieve fast, accurate, and energy-efficient performance is also an urgent challenge. This paper proposes a network for aerial object detection by incorporating an attention mechanism to enhance feature extraction and elevate the accuracy of aerial moving object detection. Additionally, we optimize the channel dimensions of the feature extraction framework, resulting in a reduction in model parameters, acceleration of inference speed, and alleviation of the computational burden. Ulteriorly, we optimize the Spatial Pyramid Pooling (SPP) module to enhance detection accuracy and processing speed. Inspired by the ResNet and RepVGG structures, we design a feature fusion module to combine early-extracted features, improving speed and accuracy. Based on the design mentioned above principles, we develop a neural network method with an impressively small model size of only [Formula: see text][Formula: see text]M. The proposed approach achieves state-of-the-art performance on five benchmark datasets. Besides its superior performance, our method demonstrates excellent throughput on edge computing devices. Experimental results show that even when running on low-performance computing devices, the CPU and GPU temperatures remain below 50∘C and achieve a detection speed of 14.8 frames per second (fps) and power consumption of only 2.9[Formula: see text]W. These findings suggest that a high-accuracy, low-power, low-latency, and low-memory footprint aerial object detection solution is achievable.

Energy-efficient resource allocation for bidirectional wireless power and information transfer over interference channels

Energy efficiency is an important performance metric for the sustainable operation of energy-limited communication networks. This study investigates an energy-efficient resource allocation strategy to maximize the energy efficiency of wireless-powered bidirectional communication over interference channels. The receivers in the system harvest energy and decode information simultaneously using a time switching or power splitting policy from data signals sent by transmitters in the forward link. The harvested energy is subsequently used to send response signals back to the transmitters in the backward link. Our objective is to jointly optimize the transmit power of the transmitters and the energy harvesting ratio of the receivers to maximize the energy efficiency of the entire system while maintaining the minimum required data rates in both the forward and backward links. Considering the non-convexity and NP-hardness of this optimization problem, we first apply nonlinear fractional programming and a dual method to derive an analytical expression for each control parameter and then propose an iterative algorithm to find suboptimal solutions with low computational complexity. The simulation results show that the proposed algorithm adjusts both the transmit power and energy harvesting ratio adaptively considering the co-channel interference and thus achieves near-optimal energy efficiency performance within a short computation time.

Energy efficiency of ecological buildings in Tunisia: Natural fiber composites and passive strategies impact

Improving the thermal insulation and energy efficiency of building envelopes is a major objective worldwide and has significantly developed in the recent years. This study aims to evaluate the impact of ecological additive and passive strategies on building energy efficiency. An experimental study was carried out to examine the effect of the incorporation of treated Alfa and Posidonia-Oceanica fibers on the thermal properties of cement and gypsum composite samples. The experimental results were then introduced in a numerical study using TRNSYS software to perform a comparison of the energy efficiency and thermal performance of three individual buildings; two ones constructed with our ecological materials and the third one with typical materials is considered as a reference case under the Tunisian climate. The obtained results indicate that the buildings built with Alfa fibers (BAF) and Posidonia-Oceanica fibers (BPOF) are economically effective since they allow a decrease of about 48.20% and 43.48% in heating, 45.71% and 42.77% in cooling, leading to a reduction in CO2 emission of 47.90% and 43.40%, respectively, in comparison with the reference case. The investigation also focuses on the improvement of the ecological building envelope by a storage wall integrated on the south front and shaded by solar movable overhangs during the summer season. The indoor climate results reveal that incorporating passive strategies into the building improves indoor air temperature and preserves a comfortable indoor relative humidity. Heating requirements decrease by 82.82% for BAF and by 79.76% for BPOF. The cooling requirements of the reference building are also reduced by 63.46% for BAF and 60.45% for BPOF by the use of natural night ventilation (4 ACH) and the appropriate shading for Trombe walls and windows. Consequently, the implementation of passive strategies on the ecological buildings led to a net reduction in CO2 emissions by up to 80.55% for BAF, compared to the reference case.

Expert Elicitation for the Resilient Design and Optimisation of Ultra-long Ore Passes for Deep Mass Mining

AbstractExtension of ore pass length has become increasingly critical for optimising energy-efficient underground mining operations. Long and ultra-long ore passes, spanning from 300 to 700 m, can significantly improve the functionality and viability of underground mass mining operations though suboptimal performance has an extremely adverse impact on production. The public domain lacks substantial information regarding the primary engineering, geological, and geotechnical risks and challenges associated with the design, implementation, operation, and maintenance of such long ore passes. Therefore, the aggregation of past experiences and the insights of experts assume paramount significance. An innovative methodology is introduced to address this evident data deficiency and to establish comprehensive guidelines for the resilient design of such lengthy ore passes — combining gap analysis with expert elicitation techniques. This equips design engineers with the necessary tools to formulate and adapt strategies for assessing the numerous challenges and uncertainties that invariably accompany their projects. Expert elicitation techniques are summarised, and a gap analysis is conducted with subject matter experts, from various countries, collating their extensive ore pass design experience, to create a comprehensive list of effective parameters and key risks that must be considered. Quantitative analysis of the survey results enabled the identification and ranking of the numerous factors affecting the design, operation, and maintenance of long and ultra-long ore passes and highlights the complex technical challenges (substantial damage from rock particle impact, increased dynamic mining stresses leading to failure, air-blasts and back blasts, dust, preferential flow, turbulent and dynamic material flow) that are uncommon in shorter ore passes. Additionally, increasing length heightens the probability of intersecting weak rock or discontinuities, leading to a higher risk of structural failure and instabilities. Faulting, folding, and large-scale structures are also critical geological factors to be considered in the design of such structures. The key geotechnical factor is also the rock type surrounding the pass. Experts highlighted the lack of clear guidelines for decision-making, resilient design, and construction so this work suggests future investigations to determine the complex interaction between the effective parameters, using approaches like the rock engineering system, discovery of cascading hazards, and optimal controls.

The rebound effect of material and energy efficiency for the EU and its major trading partners

This study presents a comprehensive analysis and comparison of material and energy efficiency performances and their rebound effects, providing insights into their implications for sustainability and climate change policies. Using Stochastic Frontier Analysis (SFA), we examine material and energy efficiency scores and rebound effects in the European Union (EU) member countries and their major trading partners from 1995 to 2019. Our findings reveal several key insights. Firstly, developed EU countries and affluent trading partners exhibit higher material and energy efficiency scores, while less developed members and partners rank lower. The study highlights the potential implications of the EU's Carbon Border Adjustment Mechanism (CBAM) for low-efficiency trading partners, which may face substantial penalties and be incentivized to take significant steps to enhance material and energy efficiency. Secondly, a positive correlation is observed between efficiency improvements and rebound effects, suggesting that policymakers should take the rebound issue seriously. Thirdly, while energy efficiency scores are relatively higher, material efficiency performances are lower and improving over the years. Finally, the rebound effects of energy efficiency exceed those of material efficiency, implying that energy reduction potentials are largely offset. Therefore, prioritizing material consumption for environmental sustainability becomes crucial due to the significant potential for improvement with lower rebound effects.

Modern architectural landscape planning and design based on environmental sensors and 3D point cloud processing

With the continuous development of modern technology, the application of sensor technology in architectural landscape planning and design is becoming increasingly widespread. The combination of environmental sensors and 3D point cloud processing technology provides new possibilities for modern architectural landscape planning and design. Environmental sensors can provide accurate environmental data support for the design process by monitoring the climate conditions of the building site in real-time, better understanding the natural environmental characteristics of the building site, and making targeted planning and design. Environmental sensors can accurately measure the intensity and direction of light, helping designers arrange the layout and window design of buildings to maximize the use of natural lighting. By monitoring and analyzing temperature data, the design team can effectively optimize the energy consumption management of buildings and improve their energy efficiency performance. The combination of 3D point cloud processing technology and environmental sensor data accurately restores the real environment of the building site to a digital model, allowing designers to conduct comprehensive spatial analysis and design. This digital and intelligent design process improves the precision and efficiency of design, allowing the design team to better understand the environmental characteristics of the building site and achieve intelligent and personalized landscape design, Injecting new vitality and creativity into future architectural planning and design.

Enhancing Energy-Efficient Water Pumping with Intelligent Control: A Comparative Study of Advanced Fuzzy Logic and PID Controllers on Simulink/MATLAB

Water resources are crucial for social and economic development, human needs, and food production. However, with water scarcity on the rise, minimizing waste during pumping is vital. Using DC motors for pumping offers a solution for sustainable and efficient water management. This study explores intelligent control strategies to enhance energy efficiency in water pumping systems, comparing Advanced Fuzzy Logic and PID Controllers through simulation. The research aims to optimize the control approach for water pumping operations, improving energy efficiency and system performance. Simulation results indicate that both controllers effectively regulate water pumping operations. The Advanced Fuzzy Logic Controller offer better response to setpoints, with minimal overshoot 0,7%, fast settling time 0.052 Seconds, Rise Time 0.048 Seconds, Time Peak 0.05 Seconds and no steady-state error, Compared to PID Controller with Overshoot 10%, Settling Time 0.23 Seconds, Rise Time 0.048 Seconds, Time Peak 0.078 Second and no steady-state error Moreover, the Advanced Fuzzy Logic Controller performs overall better performance in terms of energy efficiency and robustness compared to the PID controller. The findings suggest that the energy efficiency of water pumping systems can be significantly increased by utilizing Advanced Fuzzy Logic Controller approaches, specifically the Advanced Fuzzy Logic Controller. This study contributes to developing control methods for sustainable water management systems with potential uses in agriculture, irrigation, and water distribution.

Enhancing Efficiency and Security in MTC Environments: A Novel Strategy for Dynamic Grouping and Streamlined Management

This study presents a new strategy to improve security and efficiency in Machine-Type Communication (MTC) networks, addressing the drawbacks of the existing Adaptive Hierarchical Group-based Mutual Authentication and Key Agreement (AHGMAKA) protocol. The AHGMAKA protocol, crucial for securing communication within groups of devices with limited resources, has been found to cause significant operational delays and inefficiencies. Our proposed solution integrates advanced cryptographic methods, including an optimized Authenticated Message Authentication Code (AMAC) and lightweight encryption, sophisticated optimization algorithms for dynamic grouping, and an efficient, lightweight group management protocol. It also introduces adaptive network management strategies to customize performance according to the needs of MTC networks. The effectiveness of this approach has been validated through empirical analysis, showing considerable improvements in operational performance and energy efficiency. These improvements mark a significant step toward achieving an optimal balance between efficiency and security for MTC networks. However, the research acknowledges ongoing challenges, including the trade-off between security and efficiency and the issue of compatibility with older devices, suggesting these as areas for future study. The paper outlines potential research paths, including using machine learning for better group management, adopting post-quantum cryptographic methods, applying hardware acceleration, and pushing to standardize these technologies. This work significantly advances the field of secure and efficient communication in MTC, a critical component of the growing Internet of Things (IoT) landscape, setting the stage for future breakthroughs.

Performance analysis of a compressed air energy storage incorporated with a biomass power generation system

Compressed air energy storage technology is recognized as a promising method to consume renewable energy on a large scale and establish the safe and stable operation of the power grid. To improve the energy efficiency and economic performance of the compressed air energy storage system, this study proposes a design for integrating a compressed air energy storage system with a biomass power generation system. In the energy storage process, the feedwater from the biomass power generation system is used to cool the compressed air in the compressed air energy storage system. In the energy release process, the flue gas from the biomass power generation system is used to heat the compressed air. Besides, the compressed air from the compressed air energy storage system first works in the expander and then goes to the biomass power generation system for combustion. Based on the system simulation, the proposed system is assessed from the energy, exergy, economy, and environment perspectives. The results show that the round-trip efficiency and the energy storage density of the compressed air energy storage subsystem are 84.90 % and 15.91 MJ/m3, respectively. The exergy efficiency of the compressed air energy storage subsystem is 80.46 %, with the highest exergy loss in the throttle valves. The total investment of the compressed air energy storage subsystem is 256.45 k$, and the dynamic payback period and the net present value are 4.20 years and 340.48 k$. Besides, the proposed system’s CO2 emission is 258 kg/GWh. This study provides a new option for enhancing the performance of compressed air energy storage through the system integration.

Energy Efficiency Examination and Analysation in Wireless Sensor Networks

Wireless sensor networks (WSNs) have risen as a vital innovation empowering a horde of applications extending from natural checking to mechanization. In any case, the constrained vitality assets of sensor hubs pose critical challenges to the life span and execution of these systems. This theory presents a comprehensive audit of procedures and techniques to move vitality proficiency forward in WSNs. The survey starts by analyzing power efficient equipment plan approaches, counting low power microcontrollers, energy harvesting frameworks, and ultra low power communication conventions. Besides, it investigates energy aware steering conventions that optimize information transmission ways to minimize vitality utilization, such as Filter (Moo Vitality Versatile Clustering Progression) and its variations. Moreover, rest planning instruments are examined, which permit sensor hubs to interchange between dynamic and rest states to preserve vitality while keeping up an organized network. Additionally, the theoretical dives into the part of information conglomeration and compression procedures in decreasing excess transmissions and maintaining vitality at the arranged level. It highlights the importance of versatile obligation cycling instruments, where sensor hubs powerfully alter their obligation cycles based on natural conditions and application prerequisites. Moreover, progressions in energy efficient MAC (Medium Get to Control) conventions are analyzed, centring on conventions like IEEE 802.15.4 and their improvements for WSNs. Furthermore, the integration of machine learning and optimization calculations for energy efficient operation and asset assignment in WSNs is examined. At last, the theory concludes with a viewpoint on rising patterns and future headings in the field, counting the integration of energy harvesting advances, the improvement of self sustainable sensor hubs, and the investigation of novel communication standards such as unmistakable light communication.

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

Research of Energy Efficiency and Environmental Performance of Vehicle Power Plant Converted to Work on Alternative Fuels

The use of alternative fuels remains an important factor in solving the problem of reducing harmful substances caused by vehicles and decarbonising transport. It is also important to ensure the energy efficiency of vehicle power plants when using different fuels at a sufficient level. The article presents the results of theoretical and experimental studies of the conversion of diesel engine to alternative fuels with hydrogen admixtures. Methanol is considered as an alternative fuel which is a cheaper alternative to commercial diesel fuel. The chemical essence of improving the calorific value of alternative methanol fuel was investigated. Studies showed that the energy effect of burning an alternative mixture with hydrogen additives exceeds the effect of burning the same amount of methanol fuel. The increase in combustion energy and engine power is achieved as a result of heat from efficient use of the engine exhaust gases and chemical conversion of methanol. An experimental installation was created to study the work of a converted diesel engine on hydrogen–methanol mixtures and thermochemical regeneration processes. Experimental studies of the energy and environmental parameters of diesel engine converted to work on an alternative fuel with hydrogen admixtures have shown that engine power increases by 10–14% and emissions of harmful substances decrease.

Proposal and application of a raised multilayer flooring system and its influences on thermal environments

ABSTRACT This study discusses a raised multilayer floor (RMF) system. The RMF system is characterized by gaps between the slab and flooring material. It has been applied to a wide range of building types in Japan because it is known to excel in acoustic absorption. The thermal performance of the RMF systems was considered to be more efficient than that of a floating floor (FF) system in which a heating panel is attached to a floor slab. However, this has not been sufficiently explored. In this paper, 72-hour in-situ temperature measurement test was performed to compare the thermal performance of the RMF and FF systems. They were applied to two identical rooms in a reinforced concrete (RC) building. The tests were performed under three different heating conditions. There was no heating, air-conditioning heating, or floor heating (FH). The RMF system was found to be more effective than the FF system in terms of FH operation speed, comfort level, energy efficiency, and vertical temperature distribution. The RMF system increased the floor surface temperature about 2°C higher than the FF system. 72-hour FH operation at a 26°C setting resulted in the RMF system reducing energy consumption by approximately 10% than the FF system.

CORRIGENDUM to “Optimization of Fast-steam-assisted gravity drainage for the energy-efficient operations at a heterogeneous oil-sands reservoir”

CORRIGENDUM to “Optimization of Fast-steam-assisted gravity drainage for the energy-efficient operations at a heterogeneous oil-sands reservoir”

Magnetocaloric effect and applied refrigeration performance of La(Fe,Si)13-based compounds

As a solid-state refrigeration technology, room-temperature magnetic refrigeration has several significant advantages comparing with conventional gas compression refrigeration, such as environmental friendliness, high energy efficiency and dependable operation. In the present study, the magnetocaloric effect and applied refrigeration performance of La0.8Ce0.2Fe11.6-xMn0.15+xSi1.25Al0.1H1.8 (x = 0, 0.03, 0.06, 0.09, 0.12) compounds were studied systematically. All alloys crystallize mainly in NaZn13-type phase with trace α-Fe phase between 1.36 and 1.80 wt%. The necessary heat treatment improves the formation of main phase. As x increases, the Curie temperature (Tc) decreases gradually from 295.3 K to 273.0 K. All alloys undergo strong field-induced first-order itinerant-electron metamagnetic transition. Under 0–1.5 T, the maximum magnetic entropy changes (-ΔSM)max for La0.8Ce0.2Fe11.6-xMn0.15+xSi1.25Al0.1H1.8 (x = 0, 0.03, 0.06, 0.09, 0.12) compounds vary from 10.9 J/(kg K) to 13.5 J/(kg K). And the maximum adiabatic temperature changes (ΔTad)max vary from 2.7 K to 3.9 K. To study the applied refrigeration performance, a rotary magnetic refrigerator is designed successfully, whose refrigeration temperature and cooling power meet the requirement of cold storage. Successful large-quantity preparation, giant magnetocaloric effect and excellent refrigeration performance suggest that, La0.8Ce0.2Fe11.6-xMn0.15+xSi1.25Al0.1H1.8 (x = 0, 0.03, 0.06, 0.09, 0.12) compounds can be used as the promising candidates for magnetic refrigerants.

Synergic effect of carbon regulation policies and multi-period credit financing in global retailing with reworking

Carbon emissions regulation has become one of the most important priorities for the business sector these days to enhance customer satisfaction. Carbon emissions regulation in the retail sector is crucial to mitigate the industry's environmental impact. It promotes the company's adherence to emission standards by conducting environmentally friendly practices like energy-efficient operation and supply chain management. This study considers various carbon emissions regulation policies, especially with reworking which repairs the imperfect items in global retailing to enhance customer satisfaction. To detect defects, a machine-based autonomated inspection is used. Also, for active global retailing, multi-period credit financing is applied in the model. Under such multi-period credit financing and carbon emission regulations, we optimize cycle time, selling price, and investments for autonomated inspection and product biodegradability to maximize the profit of the retail industry. Nine different cases regarding the carbon emission policy and credit financing are studied and compared. We found that the retail industry will earn a maximum profit when cycle time is less than the first credit period under a limited carbon emissions policy which is at least 0.09% higher compared to other cases. Credit financing helps to increase the profit by 1.57%. Consequently, profit becomes the least when cycle time is greater than the second delay period under the carbon taxation policy.

Simultaneous achievement of energy-efficient operation and high thermal stability of magnetic devices by enhancement of Dzyaloshinskii-Moriya interaction and magnetic anisotropy energy

Simultaneous achievement of energy-efficient operation and high thermal stability of magnetic devices by enhancement of Dzyaloshinskii-Moriya interaction and magnetic anisotropy energy

Energy-saving operation in urban rail transit: A deep reinforcement learning approach with speed optimization

The energy consumption of urban rail transit plays a significant role in the operating costs of trains. It is particularly crucial to decrease the energy consumption of the traction power supply in subway systems, as it accounts for approximately half of the total energy consumption of the subway operating organization. To overcome the limitations of traditional real-time speed profile generation methods and the limited exploration capabilities of popular reinforcement learning algorithms in the speed domain, this paper presents the Energy-Saving Maximum Entropy Deep Reinforcement Learning (ES-MEDRL) algorithm. The ES-MEDRL algorithm incorporates Lagrange multipliers and maximum policy entropy as penalties to formulate a novel objective function. This function aims to intensify exploration in the speed domain, minimize train traction energy consumption, and ensure a balance between ride comfort, punctuality, and safety within the subway system. This leads to the optimization of speed profile strategies. To further reduce energy consumption, this paper proposes a secondary optimization strategy for the energy-saving speed profile. This approach involves trading acceptable travel time for improved energy efficiency. To validate the performance of the proposed model and algorithm, numerical experiments are conducted using the Yizhuang Line of the Beijing Metro. The findings demonstrate a minimum 20 % increase in energy efficiency with the ES-MEDRL algorithm compared to manual driving. This algorithm can guide energy-efficient train operations at the planning level.