Aspects Of Energy Consumption Research Articles

A Resilient Routing Protocol to Reduce Update Cost by Unsupervised Learning and Deep Reinforcement Learning in Mobile Ad Hoc Networks

Reinforcement Learning (RL)-based routing protocol has been proposed to establish paths in mobile ad hoc networks. However, due to the overhead of updating reward values according to frequent topology changes, existing protocols based on RL suffer from scalability problems with a large number of state and action spaces. To defeat this problem, in this paper, we propose a new resilient routing protocol by applying Unsupervised Learning (UL) prior to Deep Reinforcement Learning (DRL). In the former scheme, each node is clustered by mobility-resilient parameters. A reliable path that consists of only robust nodes in UL is decided by DRL with reasonable weight value through Multi-Objective Decision Making (MCDM). This approach leads to a reduction in update cost for reward value by excluding nodes that are considered severely affected by mobility. The comparative simulation results demonstrated that the proposed scheme outperformed the existing scheme in the aspects of Packet Delivery Ratio (PDR) and energy consumption. Our protocol demonstrates up to 35% higher PDR and reduces energy usage by approximately 20% under high-mobility conditions compared to Q-Learning-based protocols.

On the Modeling of Inverse Memristor With Completely Explicit Memory Description: A Memorized Dielectric Relaxation Analogy‐Based Approach

ABSTRACTIn this work, a memorized dielectric relaxation analogy‐based model of inverse memristor has been proposed. In contrast to the previous models, a completely explicit memory description of the inverse memristor can be achieved by ours. The proposed model has been found to be well applicable to the practical inverse memristors of both current and voltage‐controlled types where the basic properties of inverse memristor have been preserved and the strong agreements between the model‐based results and their circuit‐based benchmarks can be observed. Based on our model, a rigorous analysis of inverse memristor has been performed to give a mathematical discussion on the simulation results. The power and energy consumption aspects have also been analyzed. In addition, the effect of parasitic element has been explored and it has been found that the inverse memristor realized based on our model is more robust to such undesired effect than those realized based on the others. This is another virtue of the proposed model. The realization methodology of the inverse memristor circuit based on our model with off‐the‐shelf components and the extension of the model toward the inverse memcapacitor and inverse meminductor have also been given.

Beyond the threshold: Understanding the asymmetric effects of renewable energy on CO2 emissions

While renewable energy deployment is essential to mitigate climate change, the interplay between renewable energy consumption and environmental degradation may not be linear. The environmental aspect of renewable energy consumption may change over time, depending on the scale and technique effects. This may be due to asymmetry in the relationship. Nonetheless, most current literature either assumes linearity, or ignores the turning point of the behavioral change. This results in inconclusive empirical findings at the disaggregated level of renewable energy consumption. This paper utilizes threshold estimation technique to capture the asymmetry in the renewable energy-CO2 emissions relation in the top ten renewable energy consumers covering the period 1990–2020. The literature gap is addressed by deriving the threshold effect at the aggregate and disaggregated levels to prevent aggregation bias. Understanding the thresholds of different renewable energy sources would improve policy effectiveness and resource allocation at different consumption levels to better curb climate change. The threshold estimation technique measures total renewables, hydro, solar, wind, and others (bioenergy and geothermal) as threshold variables. The findings indicate that total renewables and solar consumptions have stronger mitigating effects on CO2 emissions beyond the consumption levels of 4363.37 and 43.58 kWh, respectively. The advantageous environmental effect of wind consumption only manifests above the consumption level of 657.40 kWh. For policy implication, this study recommends an increase in the weightage of renewables in the energy mix by formulating energy-specific policies, in order to optimize the environmental benefits of renewable energy adoption.

Energy-Efficient Handover Algorithm for Sustainable Mobile Networks: Balancing Connectivity and Power Consumption

In the era of pervasive mobile and heterogeneous networks, maintaining seamless connectivity during handover events while minimizing energy consumption is paramount. Traditional handover mechanisms prioritize metrics such as signal strength, user mobility, and network load, often neglecting the critical aspect of energy consumption. This study presents a novel approach to handover decision-making in mobile networks by incorporating energy-related metrics, such as battery level, energy consumption rate, and environmental context, to make informed handover decisions that balance connectivity quality and energy efficiency. Unlike traditional methods that primarily focus on signal strength and network load, our approach addresses the critical need for energy efficiency, particularly in high-mobility scenarios. This innovative framework not only enhances connectivity but also significantly improves power consumption management, offering a more sustainable solution for modern mobile networks. Through extensive simulations, we demonstrate the effectiveness of our proposed solution in reducing energy usage without compromising network performance. The results reveal significant improvements in energy savings for mobile devices, especially under high-mobility scenarios and varying network conditions. By prioritizing energy-efficient handovers, our approach not only extends the battery life of mobile devices but also contributes to the overall sustainability of mobile networks. This paper underscores the importance of incorporating energy metrics into handover decisions and sets the stage for future research in energy-aware network management.

Proposed Supercluster-Based UMBBFS Routing Protocol for Emergency Message Dissemination in Edge-RSU for 5G VANET

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET’s network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach’s performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures.

Study on the form of sealing ring and its sealing characteristics of centrifugal pump

Abstract The sealing ring, also known as the leakage relief ring, is usually composed of a fixed part and a rotating part, which is used to reduce the gap leakage between the rotating part of the pump and the fixed part, and its structural form and clearance value have a great impact on the stability and efficiency of the pump set. In this paper, the sealing ring between the top cover impeller of a centrifugal pump is used as the research object, and the empirical analysis method and numerical simulation method are used to analyse the water sealing characteristics of the sealing ring structure commonly used in five projects from the aspects of leakage amount and energy consumption, and compare. The results show that the labyrinth, stepped and comb tooth sealing rings have good water sealing effect. Aiming at the matching of different sealing gaps and diffusion domains of stepped sealing rings, the influence of sealing structure size on water sealing characteristics was explored, which provided a technical basis for the selection of sealing ring structure and the determination of matching size of centrifugal pump.

Modelling, Analysis and Comparison of Robot Energy Consumption for Three-Dimensional Concrete Printing Technology

The technology used for the 3D printing of buildings from concrete is currently a very relevant and developing topic and appears to be especially advantageous in terms of sustainable production. An important aspect of the sustainability assessment is the energy efficiency of the printing robots. Printing robots consume a significant amount of energy when printing. It is important to analyse this energy thoroughly and to be able to predict it in order to optimise the movement and control of printing robots to reduce energy consumption. In that paper, we analyse in detail the energy consumption of printing robots, which has not yet been thoroughly investigated in the context of 3D printing building applications. We present a methodology to develop an energy consumption model for a printing robot, specifically developed and optimized for this technology. Our methodology incorporates an innovative approach to determine reduced-efficiency maps, allowing for the inclusion of difficult-to-measure drive efficiency parameters in the model. This results in a comprehensive model of the energy consumption of the printing robot, reflecting its operating characteristics in a real-world environment. An open control system of the printing robot is used for the measurement of energy quantities, and specially developed software tools are introduced. We also present the first direct comparison of the energy consumption of different printing robots when following a uniform printing trajectory. The comparison is made based on the presented methodology to obtain and compare actual energy data from workplaces with printing robots. The methodology combines measured data with energy simulations from ABB RobotStudio, enabling energy comparisons between industrially articulated robots and real printing robots, including the ABB IRB4600, the gantry printing robot, and the printing robot. The experiments clearly demonstrate that the kinematic structure of printing robots significantly affects their energy consumption in 3D printing concrete. Based on the conducted methodologies and analyses, we identify key aspects of energy consumption of printing robots in 3D Construction Printing or 3D Concrete Printing (3DCP) technology. In doing so, we bring a new perspective and provide a basis for further research and development in this previously understudied area.

An Edge Cloud Based Coordination Platform for Multi-user AR Applications

Augmented Reality (AR) applications can reshape our society enabling novel ways of interactions and immersive experiences in many fields. However, multi-user and collaborative AR applications pose several challenges. The expected user experience requires accurate position and orientation information for each device and precise synchronization of the respective coordinate systems in real-time. Unlike mobile phones or AR glasses running on battery with constrained resource capacity, cloud and edge platforms can provide the computing power for the core functions under the hood. In this paper, we propose a novel edge cloud based platform for multi-user AR applications realizing an essential coordination service among the users. The latency critical, computation intensive Simultaneous Localization And Mapping (SLAM) function is offloaded from the device to the edge cloud infrastructure. Our solution is built on open-source SLAM libraries and the Robot Operating System (ROS). Our contribution is threefold. First, we propose an extensible, edge cloud based AR architecture. Second, we develop a proof-of-concept prototype supporting multiple devices and building on an AI-based SLAM selection component. Third, a dedicated measurement methodology is described, including energy consumption aspects as well, and the overall performance of the system is evaluated via real experiments.

Study on time-of-use pricing method for steam heating system considering user response characteristics and thermal storage capacity

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply.

Performance, Energy Consumption and Costs: A Comparative Analysis of Automatic Text Classification Approaches in the Legal Domain

The common practice in Machine Learning research is to evaluate the top-performing models based on their performance. However, this often leads to overlooking other crucial aspects that should be given careful consideration. In some cases, the performance differences between various approaches may be insignificant, whereas factors like production costs, energy consumption, and carbon footprint should be taken into account. Large Language Models (LLMs) are widely used in academia and industry to address NLP problems. In this study, we present a comprehensive quantitative comparison between traditional approaches (SVM-based) and more recent approaches such as LLM (BERT family models) and generative models (GPT2 and LLAMA2), using the LexGLUE benchmark. Our evaluation takes into account not only performance parameters (standard indices), but also alternative measures such as timing, energy consumption and costs, which collectively contribute to the carbon footprint. To ensure a complete analysis, we separately considered the prototyping phase (which involves model selection through training-validation-test iterations) and the in-production phases. These phases follow distinct implementation procedures and require different resources. The results indicate that simpler algorithms often achieve performance levels similar to those of complex models (LLM and generative models), consuming much less energy and requiring fewer resources. These findings suggest that companies should consider additional considerations when choosing machine learning (ML) solutions. The analysis also demonstrates that it is increasingly necessary for the scientific world to also begin to consider aspects of energy consumption in model evaluations, in order to be able to give real meaning to the results obtained using standard metrics (Precision, Recall, F1 and so on).

Different Efficient Responses of Sorghum and Maize Varieties to Different Irrigation Systems

Drought is one of the most common abiotic stressors in the agricultural community. The purpose of this study was to assess the effects of drought on sorghum and maize seedlings. The experiment was conducted as a randomized complete block design (RCBD) in a split plot arrangement with three replicates over two years in Isfahan, Iran. Investigational treatments include three-tier drought stress for two varieties of each plant species. The results demonstrate that the highest energy productivity of fresh forage was obtained in the Pegah variety of sorghum (1.49 kg Mj-1) and the lowest was obtained in the Maxima variety of maize (0.52 kg Mj-1).With 60% irrigation, the lowest water productivity of fresh and dry fodder in maize was found in the Maxima variety (8.32 kg m-3) and the 704 variety (2.15 kg m-3). However, sorghum in the Pegah cultivar at 60% irrigation had the highest water productivity when it came to wet and dry fodder, with an average of 25.45 kg m-3 and 7.57 kg m-3, respectively. These results clearly show that in the aspect of energy consumption and production, sorghum was able to optimally convert the consumed energy into more fodder. On the other hand, the Pegah variety in sorghum, having the highest energy efficiency of dry fodder regardless of the amount of water used, was a more suitable plant to choose for planting in areas with water shortage.

Recent and Future Advances in Water Electrolysis for Green Hydrogen Generation: Critical Analysis and Perspectives

This paper delves into the pivotal role of water electrolysis (WE) in green hydrogen production, a process utilizing renewable energy sources through electrolysis. The term “green hydrogen” signifies its distinction from conventional “grey” or “brown” hydrogen produced from fossil fuels, emphasizing the importance of decarbonization in the hydrogen value chain. WE becomes a linchpin, balancing surplus green energy, stabilizing the grid, and addressing challenges in hard-to-abate sectors like long-haul transport and heavy industries. This paper navigates through electrolysis variants, technological challenges, and the crucial association between electrolytic hydrogen production and renewable energy sources (RESs). Energy consumption aspects are scrutinized, highlighting the need for optimization strategies to enhance efficiency. This paper systematically addresses electrolysis fundamentals, technologies, scaling issues, and the nexus with energy sources. It emphasizes the transformative potential of electrolytic hydrogen in the broader energy landscape, underscoring its role in shaping a sustainable future. Through a systematic analysis, this study bridges the gap between detailed technological insights and the larger energy system context, offering a holistic perspective. This paper concludes by summarizing key findings, showcasing the prospects, challenges, and opportunities associated with hydrogen production via water electrolysis for the energy transition.

A Comprehensive Review of the Applications of Hybrid Evaporative Cooling and Solar Energy Source Systems

Recent advancements in single-stage evaporative cooling (EC) have showcased their effectiveness as an energy-efficient and sustainable air-conditioning (AC) solution. However, several challenges hinder the widespread adoption of EC in various applications. These challenges include climate sensitivity, substantial spatial requirements, and limitations in achieving desired output temperatures. To address these concerns, there has been a growing focus on integrating EC with solar energy (SE) systems. With traditional energy resources being depleted, the use of SE has gained prominence as a sustainable solution to meet future energy demands while mitigating environmental pollution. This paper presents a comprehensive review of hybrid EC–SE systems, aiming to elucidate the potential synergies, benefits, and challenges associated with this integration. The review explores the principles and mathematical approaches of various configurations of EC systems to assess their compatibility with SE sources. Furthermore, the review delves into the mathematical model of SE, encompassing both solar power generation and thermal collectors, with the aim of integrating it into the EC model. It delves into key aspects of energy consumption and performance, showcasing advancements in achieving higher efficiency and enhanced cooling capacity through the hybrid systems. Additionally, the review highlights research gaps in the existing literature, emphasizing the need for further exploration in this interdisciplinary field. In conclusion, this paper offers valuable insights into the potential of EC–SE systems to address energy and cooling requirements while promoting sustainable development.

Artificial intelligent control of energy management PV system

Renewable energy systems, such as photovoltaic (PV) systems, have become increasingly significant in response to the pressing concerns of climate change and the imperative to mitigate carbon emissions. When static converters are used in solar power systems, they change the current, which uses reactive energy. A proportional-integral controller regulates active and reactive powers, whereas energy storage batteries enhance energy quality by storing current and voltage as they directly affect steady-state error. The utilization of artificial intelligence (AI) is crucial for improving the energy generation of PV systems under various climatic circumstances, as conventional controllers do not effectively optimize the energy output of solar systems. Nevertheless, the performance of PV systems can be influenced by fluctuations in meteorological conditions. This study presents a novel approach for integrating solar PV systems with high input performance through adaptive neuro-fuzzy inference systems (ANFIS). A fuzzy neural inference-based controller regarding energy generation and consumption aspects was designed and examined. This study examines the importance of artificial intelligence in facilitating continuous power supply to clients using a battery system, hence emphasizing its significance in energy management. Moreover, the findings demonstrated promising outcomes in energy regulation and management.

Design and performance analysis of braking system in an electric vehicle using adaptive neural networks

Research article emphasizes on the impact of braking concepts considering regenerative braking system and energy consumption aspects in electric vehicles through a new perspective. The electric vehicle system is modeled and simulated using the MATLAB/Simulink software. A dataset is developed using the virtual simulation environment created by co-simulation using the MATLAB/Simulink and the IPG Carmaker software. This dataset is also used in a neural network model based on adaptive neuro fuzzy logic and the system performance is analyzed. Parameters considered for training the neural network are the brake pedal displacement, braking change rate and the need for brake application. The highlight of this study is the focus on a front wheel driven electric vehicle, which uses a standard drive cycle input to validate the model. The significant parameters evaluated in this study include the braking effects, kinetic energy, regenerative braking torque, battery state of the charge and the motor torque. The torque generation and its intended braking force requirements based on the acceleration, deceleration and braking conditions are the notable observations. The regenerative capability of this proposed system design is also illustrated along with the surface plots based on the training dataset. Investigation and analysis reveal that, the battery state of charge could be revived throughout the drive with a steady and stable increase. Transitions of motor torques between tractive and regenerative phases are also illustrated and explained for clarity and brevity.

Energy flow analysis of in Litopenaeus vannamei-Tegillarca granosa polyculture ponds based on EwE model

In order to characterize the energy flow and trophic structure of a polyculture pond that contained Litopenaeus vannamei and Tegillarca granosa, we used an Ecopath with Ecosim (EwE) model. We constructed the EwE model using 13 functional groups including L. vannamei, T. granosa, macrozooplankton, microzooplankton, microbenthos, bacterioplankton, benthic bacteria, micro-phytoplankton, nano-phytoplankton, pico-phytoplankton, artificial feed, detritus in water, and detritus in sediment. Our findings showed that this polyculture ecosystem contained five trophic levels and that two of them accounted for 99.41% of the total system throughput. L. vannamei, microbenthos, and detritus in sediment displayed high ecotrophic efficiency (EE) values, while artificial feed held relatively low EE implying that the artificial feed was used inefficiently. The bacteria including benthic bacteria and bacterioplankton was inefficiently used. The main energy flows were associated with artificial feed, phytoplankton, and detritus in this polyculture system. The compatibility of L. vannamei and T. granosa was high according to the mixed trophic impact analysis. In conclusion, the study showed that this polyculture ecosystem still needed to be improved in the aspects of energy consumption. It would be necessary for this polyculture ecosystem to perform further optimization and enhancement on the energy utilization efficiency and comprehensive benefits.

Comprehensive assessment of building energy consumption in hot summer and cold winter areas based on carbon tax considerations

With the rapid development of economy and urbanization in China, cities are expanding rapidly and more constructions are being built, then lead to the fast-growing part in urban residential energy consumption. In China’s hot summer and cold winter regions, many buildings are not centrally heated and commonly rely on electrical equipment such as air conditioners which are all energy-inefficient thermoregulation devices. In order to analyze the relationship between building energy consumption and the energy efficiency ratio (EER) of air conditioning and the area of photovoltaic (PV) on the roof, the influence of the building envelope on building energy consumption in hot summer and cold winter regions in China is clarified. This paper uses energy plus software to analyze the impact of different EER and PV area on building energy consumption using a typical case study of a public health upgrading project in the eastern part of the Wuxing district, Huzhou, Zhejiang province in China. The simulation results show that the factor that has a greater impact on the energy consumption of the health center in summer is the PV area parameter of the building compared to the performance parameters of the air conditioning equipment. The PV area parameter settings and air conditioning equipment performance adjustments are dependent on the actual situation and the comprehensive energy saving rate of the outpatient building for public health in the eastern part of Tai Wu Xing District can exceed 50%. Finally, an economic analysis of the carbon tax and input costs is carried out, and the best combination is of which 60% of the area covered by Longi Green PV panels and an air conditioning efficiency ratio of 4.87. The research result combines renewable energy and efficient equipment to achieve dual optimization of environmental and economic aspects of building energy consumption, while providing reference opinions on the comprehensive evaluation method of building energy consumption from the perspective of carbon tax.

An improved k-NN anomaly detection framework based on locality sensitive hashing for edge computing environment

Large deployment of wireless sensor networks in various fields bring great benefits. With the increasing volume of sensor data, traditional data collection and processing schemes gradually become unable to meet the requirements in actual scenarios. As data quality is vital to data mining and value extraction, this paper presents a distributed anomaly detection framework which combines cloud computing and edge computing. The framework consists of three major components: k-nearest neighbors, locality sensitive hashing, and cosine similarity. The traditional k-nearest neighbors algorithm is improved by locality sensitive hashing in terms of computation cost and processing time. An initial anomaly detection result is given by the combination of k-nearest neighbors and locality sensitive hashing. To further improve the accuracy of anomaly detection, a second test for anomaly is provided based on cosine similarity. Extensive experiments are conducted to evaluate the performance of our proposal. Six popular methods are used for comparison. Experimental results show that our model has advantages in the aspects of accuracy, delay, and energy consumption.

Joint task offloading and resource allocation in mixed edge/cloud computing and blockchain empowered device-free sensing systems

Recent studies have shown that wireless signals can not only be used as communication medium for data transmission, but also can be employed for device-free sensing (DFS). However, the sensed raw big data processing needs large amount of processing resources. Moreover, the sensed data are usually privacy-sensitive, where blockchain can be employed to guarantee the privacy related issues, and however, blockchain tasks are usually also computation-intensive. To provide the processing capabilities for sensed big data processing and blockchain task executing in DFS, mobile edge computing (MEC) and cloud computing are essential enabling technologies. In this paper, we consider a multiuser mobile offloading network consisting a edge node (or fog node) and a remote cloud server. The tasks could be processed locally, or offloaded to the edge nodes, or further migrated to the cloud relayed by the edge node. We formulate the offloading problem as the joint optimization of task offloading decision making of all the users, the computation resource allocation among the edge-executing applications, and radio resource assignment among all the remote-processing applications, aiming to minimize the maximum weighted cost of all the users. It is demonstrated that the problem is NP-hard. To tackle this challenge, we decoupled the problem into subproblems, where offloading decisions are obtained using semi-definite relaxation (SDR), and next, a swarm intelligence algorithm, i.e., fireworks algorithm, is adopted in radio resource allocation. Simulation results exhibit that as a result of the collaboration of the collaborate of edge and cloud, the proposed joint algorithm could achieves nearly optimal performance in the aspects of energy consumption and delay compared with other benchmark algorithms.

Evaluation of operation cost and energy consumption of ports: comparative study on different container terminal layouts

Automatic container terminals (ACTs) have emerged as the development direction of upgrading and transformation for traditional manual container terminals. A number of terminal layouts are devised and implemented. However, it is the lack of a comprehensive comparison of different container terminal layouts from aspects of operation cost and energy consumption. In view of this, a Time-Driven Activity-Based Costing approach is used to identify the ACT profitability bottlenecks. A multi-agent based simulation model considering four main terminal layouts is established. The multi-level handling of ACTs is simulated and the corresponding operation time and energy consumption are calculated. Simulation results show that automated guided vehicles (AGVs) are the key to improving the cost-effectiveness of ACTs. The cost-effectiveness of ACTs with non-cantilever automated rail-mounted gantries (ARMGs) is more vulnerable than that of ACTs with bilateral cantilever ARMGs. On average, ACTs with bilateral-cantilever ARMGs consume less energy per container task than ACTs with non-cantilever ARMGs. This study provides management insights into resource allocation, layout design, and sustainable development.