Energy Efficiency Optimization Research Articles

Robust IRS-Element Activation for Energy Efficiency Optimization in IRS-Assisted Communication Systems With Imperfect CSI

Robust IRS-Element Activation for Energy Efficiency Optimization in IRS-Assisted Communication Systems With Imperfect CSI

Robust Energy Efficiency Optimization for Double-RIS-Assisted Wireless-Powered Backscatter Communications

Robust Energy Efficiency Optimization for Double-RIS-Assisted Wireless-Powered Backscatter Communications

Freshness-Aware Energy Efficiency Optimization for Integrated Access and Backhaul Networks

Freshness-Aware Energy Efficiency Optimization for Integrated Access and Backhaul Networks

Energy-efficient optimization design of bio-butanol fermentation broth purification process

To address the downstream processing challenges of the IBE (isopropanol-butanol-ethanol) system and obtain biobutanol products with a mass fraction of 0.9999, as well as obtain a mass fraction of 0.99975 for the IE (isopropanol-Ethanol) mixture product as a gasoline additive, this study proposes a four-column distillation process termed "Dehydration-Butanol-Extractive Four Column Distillation" (DBE-4CD). With the heat load as the optimization target, the DBE-4CD process was optimized to determine the optimal operating parameters. Based on the optimized process and considering the energy-saving potential of the dividing wall column, an “Azeotropic Dividing Wall-Extractive Three Column Distillation” (ADE-3CD) process was subsequently proposed to further enhance energy efficiency and reduce consumption. Compared to both conventional literature process and the DBE-4CD process, the total load of the ADE-3CD process decreased to 7433.5 kW, representing reductions of 20.35% and 10.11%, respectively. Additionally, the mass recovery rates of butanol and the IE mixture reached 99.90% and 99.64%, respectively, exceeding those of the conventional literature process, which were 99.11% and 99.18%.

Energy-efficient optimization for secure wireless-powered backscatter communications with imperfect CSI and artificial noise

Energy-efficient optimization for secure wireless-powered backscatter communications with imperfect CSI and artificial noise

Dynamic Performance and Power Optimization with Heterogeneous Processing-in-Memory for AI Applications on Edge Devices

The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI applications on edge devices requires a careful balance between data processing performance and energy efficiency. This challenge becomes even more critical when the computational load of applications dynamically changes over time, making it difficult to maintain optimal performance and energy efficiency simultaneously. To address these challenges, we propose a novel processing-in-memory (PIM) technology that dynamically optimizes performance and power consumption in response to real-time workload variations in AI applications. Our proposed solution consists of a new PIM architecture and an operational algorithm designed to maximize its effectiveness. The PIM architecture follows a well-established structure known for effectively handling data-centric tasks in AI applications. However, unlike conventional designs, it features a heterogeneous configuration of high-performance PIM (HP-PIM) modules and low-power PIM (LP-PIM) modules. This enables the system to dynamically adjust data processing based on varying computational load, optimizing energy efficiency according to the application’s workload demands. In addition, we present a data placement optimization algorithm to fully leverage the potential of the heterogeneous PIM architecture. This algorithm predicts changes in application workloads and optimally allocates data to the HP-PIM and LP-PIM modules, improving energy efficiency. To validate and evaluate the proposed technology, we implemented the PIM architecture and developed an embedded processor that integrates this architecture. We performed FPGA prototyping of the processor, and functional verification was successfully completed. Experimental results from running applications with varying workload demands on the prototype PIM processor demonstrate that the proposed technology achieves up to 29.54% energy savings.

Evaluating conventional and renewable energy systems for green buildings: A case study on energy efficiency and cost optimization

This study explores two primary categories of power generation: conventional methods reliant on fossil fuels and renewable energy methods. Fossil fuel-based approaches, including natural gas and heavy fuel oil, contribute to environmental concerns due to their carbon dioxide (CO2) emissions, which are responsible for the greenhouse effect and global warming. Many nations, particularly in Europe, have implemented stringent measures to restrict CO2 emissions, imposing fines on industries that exceed emission limits. In response to these concerns, this study focuses on a green building project that aims to meet its power requirements through renewable energy sources. Various renewable energy resources will be evaluated, considering the merits and drawbacks of each technology, to identify the most suitable option for the building under investigation. Additionally, a comprehensive thermodynamic analysis will be conducted, including calculations of electrical needs such as cooling load, lighting load, and equipment load. A customized power calculator will also be developed for application in similar projects. The ultimate objective of this study is to achieve complete autonomy from the electric grid by utilizing renewable energy resources, transforming the building into a green building. The results indicate that at an outside temperature of 22 °C, the total heat gain decreases from 157.3 to 39 kW, reducing the total cost of electricity from 20,000 to 5100 AED/year. Conversely, at an outside temperature of 50 °C, the total heat gain increases from 157.3 to 196 kW, raising the total cost of electricity from 20,000 to 26,000 AED/year. These findings demonstrate the significant influence of outside temperature on the building's energy requirements and costs, underscoring the importance of considering temperature variations in energy efficiency and cost optimization strategies.

3D printed optimized electrodes for electrochemical flow reactors

Recent advances in 3D printing have enabled the manufacture of porous electrodes which cannot be machined using traditional methods. With micron-scale precision, the pore structure of an electrode can now be designed for optimal energy efficiency, and a 3D printed electrode is not limited to a single uniform porosity. As these electrodes scale in size, however, the total number of possible pore designs can be intractable; choosing an appropriate pore distribution manually can be a complex task. To address this challenge, we adopt an inverse design approach. Using physics-based models, the electrode structure is optimized to minimize power losses in a flow reactor. The computer-generated structure is then printed and benchmarked against homogeneous porosity electrodes. We show how an optimized electrode decreases the power requirements by 16% compared to the best-case homogeneous porosity. Future work could apply this approach to flow batteries, electrolyzers, and fuel cells to accelerate their design and implementation.

Drive force allocation control based on pump displacement optimization for hydraulic hybrid motor-assisted system

To achieve rational coordination of the front and rear axle torque of commercial vehicles equipped with hydraulic hybrid motor-assisted systems (HHMAS), and further enhance the vehicle’s fuel efficiency and traction capability. A hydraulic pump displacement control strategy based on energy-efficiency-orientation (EEO) is proposed for driving force distribution. Firstly, the efficiency is analyzed from the perspective of system energy flow. Then, considering the time-varying efficiency characteristics of the hydraulic variable pump and hydraulic motor, the stochastic dynamic optimization problem is modeled as a markov decision process, and a value function estimation method is introduced to approximate the optimal energy efficiency of the entire HHMAS. Finally, the results indicate that the EEO strategy can enhance the fuel economy of vehicles, with a fuel-saving rate of 6.3% compared to the wheel-speed-following-control (WSFC) strategy. In addition, the proposed strategy exhibits superior traction capacity and climbing ability compared to the traditional WSFC strategy.

AN ANALYSIS ON THE USE OF MODIFIED EXPANDED PERLITE AND PUMICE IN INORGANIC BONDED FIBROUS COMPOSITE BOARDS

It is often stated that there is an energy efficiency difference between optimum energy use and actual energy use in the world. In the construction industry, various building materials are produced and used to optimize energy efficiency in buildings. Among these building materials, inorganic bonded fibrous composite boards, whose energy efficiency criteria have begun to be improved, are widely used both in Türkiye and in the world. This article presents an experimental analysis of the utilization of modified expanded perlite and pumice as key constituents in the development of inorganic bonded fibrous composite boards. The study investigates the influence of these modified porous materials on the physical, mechanical, and thermal properties of the composite boards. For this purpose; composite mortars were produced using micronized quartz sand, a hybrid fiber consisting of cellulose and glass fiber, modified expanded perlite (MEP) with stearic acid (1, 2, 3, 4, 5, 6, 7, 8, 9 wt.%) and modified pumice (MPU) with stearic acid (1, 2, 3, 4, 5, 6, 7, 8, 9 wt.%). In order to make a comparison, a control mortar that did not contain modified expanded perlite and modified pumice was produced. Through a series of experiments, it is concluded that the density values of all other mixture designs with MEP and MPU aggregate additives under equivalent conditions are lower than the control sample. The water absorption values of the samples always remained below the control sample, and with the increase in the MPU ratio and decrease in the MEP ratio, the water absorption values of the samples also decreased. The average modulus of rupture (MOR) value of control sample in the analysis made after 14 days of curing under ambient conditions is 3.73 MPa. The highest MOR value of the test samples is 3.51 MPa, which is the mixture using the highest MPU. The thermal conductivity value of the control mixture is 0.352 W/mK. The thermal conductivity value of test mixtures with MEP and MPU aggregates varies between 0.175 W/mK and 0.287 W/mK.

ENIAO: Energy Aware Faulty Node Re-Placement Integrated With Duty Cycling and Improved Harmony Search Based Clustering through Adaptive Fish School Search Routing for WSN Optimization

In wireless sensor networks (WSNs), sensor nodes are constrained by resource constraints. The limited energy supply and susceptibility to failure greatly affect WSN lifespan, hindering long-term deployment. ENIAO is an Integrated cross-layer Optimized Routing Approach for WSNs that is fault-tolerant and energy-efficient. A bio-inspired clustering architecture and adaptive duty cycling are incorporated into ENIAO's routing optimization. In a clustering protocol, the network is partitioned, and paths are dynamically optimized within and between clusters. By optimizing active/sleep schedules, duty cycling optimizes energy efficiency. A variety of network conditions have been simulated to assess ENIAO's performance. Regarding fault tolerance and energy consumption, ENIAO significantly prolongs the network lifetime. As compared to benchmark protocols, it achieves higher throughput. As a result of the cross-layer design, ENIAO is automatically adapted to optimize energy usage and routing reliability. In the long run, large-scale IoT deployments are possible with ENIAO due to the integrated approach.

A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning

In the air-to-ground transmissions, the lifespan of the network is based on the “unmanned aerial vehicle's (UAV)” life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate’s minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the “Internet of Things (IoT)” systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.

Hybrid intelligent algorithm aided energy consumption optimization in smart grid systems with edge computing

The rapid proliferation of smart grid systems necessitates efficient management of energy resources, particularly in the context of mobile edge computing (MEC) networks. This paper presents a novel approach to optimize the energy consumption in smart grid systems with the integration of edge computing, employing a hybrid intelligent algorithm (HIA) empowered by particle swarm optimization (PSO). The primary objective is to enhance the sustainability and operational efficiency of smart grid infrastructures by minimizing the energy consumption in the MEC networks. The proposed HIA utilizes PSO to dynamically allocate computational tasks and manage resources among edge devices based on real-time demand fluctuations. This adaptive approach aims to achieve the optimal load balancing and energy efficiency across the smart grid ecosystem. By leveraging the PSO’s ability to iteratively refine solutions and adapt to changing environmental conditions, the algorithm optimizes the energy consumption while maintaining requisite service levels and reliability. Simulation experiments and case studies validate the effectiveness of the proposed PSO-based HIA in reducing the energy consumption without compromising system other performances. The results demonstrate substantial improvements in the energy efficiency, illustrating the feasibility and benefits of employing intelligent algorithms tailored for edge computing environments within smart grid systems. This research contributes to advancing sustainable smart grid technologies by introducing a robust framework for energy optimization through hybrid intelligent algorithms.

A novel high-precision and self-adaptive prediction method for ship energy consumption based on the multi-model fusion approach

The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency.

Energy Efficient and Throughput Oriented Route Optimization Models in the Internet of Vehicles: A Survey

The Internet of Vehicles (IoV) has emerged as a promising paradigm that integrates vehicles, communication technologies, and the Internet to foster intelligent transportation systems. To fully exploit the potential of IoV, efficient route optimization models are crucial to optimize energy consumption while ensuring high throughput. This survey paper aims to provide a comprehensive analysis of existing research on energy-efficient and throughput-oriented route optimization models in IoV. The primary objective of this survey is to categorize and evaluate various route optimization techniques that focus on enhancing energy efficiency and throughput in the IoV context. We present a systematic review of literature, encompassing academic papers, conference proceedings, and technical reports up to the time of this research. By examining the state-of-the-art approaches, we identify the underlying principles, strengths, and limitations of each method. The survey first delves into the foundational concepts of IoV and their significance in modern transportation systems. We elucidate the challenges faced in IoV route optimization concerning energy consumption, data transformation, bandwidth contention, and network congestion. In the main body of the survey, we classify existing route optimization models based on their energy efficiency and throughput-oriented objectives. The energy-efficient category encompasses methodologies that aim to minimize a node’s energy consumption and extend its battery life, considering factors such as the routing process involved (massage broadcasting and rebroadcasting), link state, and traffic flow patterns. On the other hand, the throughput-oriented category focuses on maximizing information transformation and ensuring low latency during operational time. Furthermore, we identify open challenges and research gaps in the field of energy-efficient and throughput-oriented route optimization for IoV. These gaps pave the way for future research directions, which can lead to the development of more robust, scalable, and adaptive routing solutions. By evaluating the strengths and weaknesses of various approaches, we aim to inspire researchers, engineers, and entrepreneurs to develop innovative solutions that enhance energy efficiency, maximize throughput, and foster the realization of a smarter and more sustainable transportation ecosystem.

Correction to: An Energy Efficiency Optimization for Uplink Communication in IEEE 802.11ah Under Restricted Access Window

Correction to: An Energy Efficiency Optimization for Uplink Communication in IEEE 802.11ah Under Restricted Access Window

A comprehensive design pipeline for FCEV powertrain configuration considering the evolutionary development process and extreme economic performance

Due to stringent emission regulations, the zero-emission hydrogen fuel cell electric vehicles (FCEVs) are gaining significant attention, but their slow power response requires careful selection of auxiliary energy sources and power systems to optimize energy efficiency. This paper presents a comprehensive design pipeline for the FCEV powertrain, encompassing methods for powertrain configuration design, feasible domain design for component parameters, and parameter matching. Initially, a powertrain configuration design method based on evolutionary game theory is introduced, forecasting trends in energy-efficient powertrain configurations using a dynamic cost function that adjusts based on the proportions of different configurations. Next, a feasible domain design method is developed to establish fixed constraints on component parameters, ensuring a fair balance between performance needs and overall costs, including development and lifecycle expenses associated with the powertrain configuration. Subsequently, dynamic programming (DP) is used for parameter matching, assessing the utmost energy-saving potential. The cost function in this method incorporates the degradation cost of powertrain components, underscoring the economic performance of the target FCEV over its lifespan. Finally, simulation assessments indicate that the proposed design pipeline effectively identifies the optimal powertrain configuration for FCEVs, endowing each possible configuration with exceptional economic efficiency across different component parameters.

BIM and orthogonal test methods to optimize the energy consumption of green buildings

The construction industry’s rapid growth significantly impacts energy consumption and environmental health. It is crucial to develop optimization strategies to enhance green building energy efficiency and encompass comprehensive analysis methods. This study aims to introduce and validate a novel framework for optimizing energy efficiency design in green buildings by integrating Building Information Modeling (BIM) technology, Life Cycle Cost (LCC) analysis, and orthogonal testing methods, focusing on enhancing energy efficiency and reducing life cycle costs. The optimization parameters for the building envelope are identified by analyzing energy consumption components and key green building factors. The orthogonal testing method was applied to streamline design options. Building Energy Consumption Simulation (BECS) software and LCC analysis tools were employed to calculate each optimized option’s total annual energy consumption and the current life cycle costs. Using the efficiency coefficient method, each optimization scheme’s energy consumption and economic indicators were thoroughly analyzed. The framework’s validity and applicability were confirmed through an empirical analysis of a campus green building case in Fujian Province, demonstrating that the optimized framework could reduce energy consumption by 4.85 kWh/m2 per year and lower costs by 38.89 Yuan/m2 compared to the reference building. The case study highlights the framework’s significant benefits in enhancing environmental performance and economic gains. The results provide critical parameter selection and offer scientific and technological support for the design of building energy efficiency, promoting optimization techniques and sustainable development within the construction industry.

Optimizing Energy Efficiency in 6G Communication Networks Based on Data Transmission Rate Allocation

To progress communication and sensing in recent years, the Sixth-Generation (6G) network was developed. To transform the technological landscape of model transmission networks we use the 6G network, and it also used to enhance the speed, decrease the latency and improve the connectivity of network. Yet, in the 6G infrastructure with these developments it has severe challenge, that is the 6G network does not negate the benefits of energy consumption associated with network. In 6G communication the sensing performance of the smart device only relay on the available power of the network, and in the 6G communication energy efficiency becomes crucial. To progress the energy efficiency of data transmission in 6G communication networks, we deployed a Data Transmission Rate Allocation (DTRA) method. Moreover, to progress the data transmission of efficient nodes we deploy a Residual Energy Cluster Head (RECH) method. In addition, to improve the reliability and speed in the 6G network we use Dynamic Multipath Routing Protocol (DMRP) method, and it also mitigate the channel defects. Atlast, in the 6G communication networks we certify QoS through optimizing the transmission rate of the user equipment through low energy consumption, and quality of channel by the use of DTRA method. Finally, in this paper we evaluate the metrices performance of the existing methods and proposed method, for instance: efficiency, energy consumption, network lifetime, and transmission speed. After evaluate the performance metrices the DTRA method enhance network lifetime and energy efficiency of 95.3% based on 6G communication networks.

Numerical study of the porous cavity with different square size vanes with a high focus on Nusselt number

This study extensively analyzes various regular and irregular vanes in pipe porous cavities on natural convection, thermal entropy generation, stream function, and temperature distribution in fluid and solid phases. The finite element method (FEM) is employed to study stream function, temperature distribution in the fluid phase and solid phase, and various γ for Nuf, ave and Nus, ave in SR, TR, SIR, and TIR. In the present study, a significant contribution of this research is the investigation into the effects of regular versus irregular vanes in the context of enclosures formed by pipe porous cavities that the SIR specimen has the most influence on stream function and thermal effect in the fluid phase and solid phase that aims to enhance system performance and optimize energy efficiency. In addition, the significant influence of geometrical parameters constitutes many heated obstacles in the middle of SIR, various heated vanes in the left of SIR, and employed Ra and ε in the analysis of Nuf, ave and Nus, ave in SIR are carried out to investigate the percentage discrepancies obtained, notably 89.35 % and 89.72 %, respectively. In validation, the calculation in results was adapted accurately to the finite element method's stream function, the temperature distribution in the fluid phase and solid phase, and various γ for Nuf, ave and Nus, ave which means that the percentage differences in obtaining results reached under 1 %. Numerical results revealed that the Hartman number has a significant influence in Shtf, ave, Shts, ave, Tyave, Nuf, ave and Nus, ave in TR, SR, TIR, and SIR.