Energy Efficiency Optimization Research Articles

A device to device driven approach towards optimizing energy efficiency for 6G networks

Our study aims to develop more energy-efficient mobile communication systems through the exploration of the 6th generation (6G) technology that is expected to be implemented in 2033. We focus on the impact of device-to-device (D2D) communication on power efficiency, which is a crucial need in this domain. To achieve this, we conducted a pioneering experiment using an in-house testbed and K-means clustering to classify locations as D2D enabled or disabled. Our findings show that there is a dynamic clustering mechanism that enables certain nodes to sustain D2D functionality around temporary base stations, resulting in a remarkable 5% improvement in network lifetime per second. This research not only enhances our understanding of 6G networks but also provides a practical methodology for optimizing energy consumption, which holds significant implications for society in advancing sustainable and efficient communication.

Optimisation of energy efficiency of ambient backscatter communication and reconfigurable intelligent surfaces in non‐orthogonal multiple access downlink

AbstractThe authors study the energy efficiency (EE) of ambient backscatter communication (AmBC) device‐assisted and reconfigurable intelligent surfaces (RIS)‐assisted non‐orthogonal multiple access (NOMA) downlinks. The authors establish two optimisation problems based on the two collaborative devices (AmBC devices, RIS) with the objective of maximising the EE of the system, taking into account the requirements of power limitation and rate limitation, etc. and also obtain the solutions of two problems by optimising the relevant performance metrics based on the alternating optimisation algorithm. For the backscatter device (BD)‐aided downlink NOMA network, the problem is first decoupled into three subproblems, where the power allocation optimisation subproblem is solved by using the quadratic transformation method and the subgradient algorithm. The maximum EE is obtained by iterating according to the Dinkelbach's algorithm. For the RIS‐aided downlink NOMA network, the power allocation problem is solved by the same method as above and the phase optimisation problem is solved by the successive convex approximation method. Numerical results show that the proposed algorithm can achieve convergence after several iterations, and the EE of systems with BD‐assisted and RIS‐assisted have different levels of sensitivity to different influencing factors.

A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method

Establishing an accurate fuel consumption prediction model is essential to optimize energy efficiency in wing-diesel hybrid ships. This study proposes an improved Blending ensemble learning method for predicting fuel consumption. Initially, the original data is cleaned using artificial experience and Interquartile Range (IQR) method. A high-dimensional feature selection method, variance analysis- Pearson correlation-mutual information-permutation importance (VA-PC-MI-PI), is then proposed to identify 16 key features from the initial 111 features. Furthermore, 9 algorithms are employed for prediction analysis under different temporal resolution data conditions. Based on the results, optimal time step and algorithm models are determined to construct the Blending ensemble model (BEM). Subsequently, the feature data of “sailing speed” and “bow draft”, which account for the highest permutation importance, are imported into the metadata set to alleviate insufficient BEM data utilization. Additionally, Extra Trees (ET) model is chosen as the meta-learner for adaptation and Bayesian method is employed for global optimization. The resulting improved Blending ensemble model (IBEM) exhibits optimal prediction accuracy and generalization ability with an RMSE of 66.04 kg/h, representing a reduction of 16.99% and 13.86% compared to the Support Vector Machine (SVM) model and BEM, respectively. Additionally, the IBEM exhibits significantly smaller residuals and absolute residuals, demonstrating superior predictive stability and consistency. This study provides an important reference and basis for energy efficiency prediction, control, and optimization of wing-diesel hybrid ships.

Comparative Assessment of Amine-Based Absorption and Calcium Looping Techniques for Optimizing Energy Efficiency in Post-Combustion Carbon Capture

<p>The escalating levels of atmospheric CO2 have underscored the necessity for developing effective carbon capture and storage (CCS) technologies. This study investigates the advancements in post-combustion CO2 capture technologies, specifically examining the efficiency of amine-based absorption and calcium looping methods. Amine absorbents were synthesized using three amino acids—Lysine, Alanine, and Arginine—supplemented with and without NaOH/KOH additives. Absorption trials were conducted in a bench-scale column across a range of temperatures. The calcium looping process involved repeated carbonation and calcination cycles using CaO to capture and release CO2. A statistical analysis employing ANOVA, was utilized to determine the influence of variables such as amine concentration, base concentration, and temperature on the efficiency of CO2 absorption. The study found that Lysine-based absorbents, adding NaOH, achieved a CO2 capture rate of up to 75% at a temperature of 30°C. Additionally, the calcium looping method exhibited consistent cyclic capacities for over 25 cycles, with a regeneration energy requirement estimated at 3.5 GJ/ton of CO2. While the amine-based systems demonstrated higher capture rates, they also required significant energy for solvent regeneration. The statistical analysis confirmed that amine concentration, base concentration, and temperature are critical factors influencing the efficiency of CO2 absorption. The findings of this study underscore the potential of optimized amine solutions and calcium looping as viable strategies for post-combustion CO2 capture, contributing valuable insights that promote sustainable practices in climate change mitigation.</p>

CONCEPT SOLUTION OF AUTONOMOUS IOT SMART HIVE AND OPTIMIZATION OF ENERGY CONSUMPTION USING ARTIFICIAL INTELLIGENCE

In this paper the authors present a conceptual solution for an autonomous smart beehive with a focus on energy efficiency; the hive's existence is based on artificial intelligence. The hive is equipped with an advanced system for monitoring the entry and exit of bees, as well as for collecting data on the weather inside and around the hive. Using an array of sensors controlled by Espressif ESP32 and Arduino Mega microcontroller boards, the hive continuously optimizes the operation of the ventilation system and other components, monitoring energy consumption and adapting to changing conditions. Special accents in the work are dedicated to the monitoring of the solar panel and, consequently, the capacity of the battery for independent power supply of the system, as well as the application of artificial intelligence to predict meteorological changes and optimize energy efficiency. This paper provides a comprehensive overview of the solutions and technologies that enable the autonomous and energy-efficient functioning of the Smart Hive.

XplAInable: Explainable AI Smoke Detection at the Edge

Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.

Optimizing Energy Efficiency in Opportunistic Networks: A Heuristic Approach to Adaptive Cluster-Based Routing Protocol

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively.

Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Optimization of Energy Efficiency in Reinforced Concrete Constructions by Using a Panel of Recycled Rubber as a Thermal Insulator

Optimization of Energy Efficiency in Reinforced Concrete Constructions by Using a Panel of Recycled Rubber as a Thermal Insulator

Optimizing energy efficiency: predicting heating load with a machine learning approach and meta-heuristic algorithms

Optimizing energy efficiency: predicting heating load with a machine learning approach and meta-heuristic algorithms

Topological and multi-objective optimization of single-phase heat transfer and energy efficiency using manifold micro-channels for high-power electrics cooling

To achieve energy-efficient cooling of high-power electrics, present work demonstrates combined methods using topological design and multi-objective optimization to reconstruct manifold micro-channels (MMC). The micro-channel domain is first topologized by density-based method under pressure drop and energy dissipation constraints. The multi-objective optimization is subsequently applied to optimize structure and operating parameters of reconstructed MMC using kriging model and genetic algorithm. The simulation results show that favorable thermal performance is obtained in the topological MMC with branch-channels, pin fins and root-channels, increasing average heat transfer coefficient by 19.4 % compared to conventional MMC. For the optimal flow rate, the lowest total thermal resistance (Rtotal) of 0.17 KW−1 is obtained for the optimized-topological MMC at a pumping power (Pp) of 9.53 mW, meanwhile the maximum temperature-rise is decreased by 15.2K. Furthermore, an extensive benchmark of different single-phase micro-fluidic cooling technologies is performed. For an extremely low Rtotal of 0.2 KW−1, present optimized-topological MMC only requires Pp of 3 mW, which is 11.7 times, 26.7 times and 70 times lower than that of conventional MMC, pin-fin and strip-fin micro-fluidic systems. This superior hydro-thermal performance successfully manages the heat flux of 100 W/cm2 to 400 W/cm2 with cooling coefficient of 104 to 107, significantly outperforming other micro-fluidic cooling technologies.

Optimal Rotor Design and Analysis of Energy-Efficient Brushless DC Motor-Driven Centrifugal Monoset Pump for Agriculture Applications

The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design and analysis of an energy-efficient BLDC motor with specifications of 1 hp, 3000 rpm, and 48 V, specifically tailored for a centrifugal monoset pump for irrigation. The focus lies in achieving optimal energy efficiency through grey wolf optimization (GWO) algorithm in the rotor design to determine optimal dimensions of the Neodymium Iron Boron (NdFeB) magnet as well as its grade. The finite element method analysis software, MagNet, is used to model and analyze the BLDC motor. The motor parameters, such as speed, torque, flux functions, temperature, and efficiency, are analyzed. For performance comparison, the same model with different magnet models is also analyzed. Validation via 3D finite element analysis highlights improvements in magnet flux linkage, stator tooth flux density, and rotor inertia with increased magnet thickness. Simulation results affirm the consistent performance of the designed BLDC motor, preferably when efficiency is increased. This efficiency and the constant speed lead to an improvement in the overall conversion efficiency of 7% within its operating range, affirming that the motor pump system is energy-efficient.

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

Energy, Exergy, Exergoeconomic, and Environmental (4E) Analysis of the Existing Gas Turbine Power Plants in BOB - PT. Bumi Siak Pusako Pertamina

The power plants in Indonesia are mostly used to supply energy for the industrial sector, including the upstream oil and gas as well as mining companies. Several companies operating in Riau contribute to the status of the region as the largest oil producer in Indonesia. These companies rely on self-generated electricity for their operations with subsequent impact on the environment. Therefore, this research was conducted to analyze the flow of energy, exergy, exergoeconomic, and environment at the 6 MW power plant operated by BOB - PT Bumi Siak Pusako - Pertamina Hulu. The second law of thermodynamics was used to evaluate energy efficiency as the maximum achievable effort. This was further integrated with economic principles to appraise the useful and wasted costs associated with thermodynamic systems through the concept of exergoeconomics. The results showed that the thermal efficiency of the gas turbine power plant was 42.85% and the exergy efficiency was 33.22% with the largest loss recorded in the combustion chamber to be 3.091 MW in the form of vibration, friction, or expansion of the components. It was also discovered that the exergy efficiency of each component was above 75%, thereby indicating the components of the gas turbine power plant components were in good condition. Moreover, the largest exergy destruction cost was 2349.16 USD/h and the exergy cost was 3,778.05 USD/kWh. The exhaust emission generated by the gas turbine power plant was 0.21 kg/s or equivalent to 0.1425 kg/kWh requiring a forest area of 11.63 ha. The results showed that the analytical method used could be comprehensively developed and applied to other power plants in Indonesia. It could also be used to understand system performance, identify energy losses, optimize energy efficiency, and link economic aspects with energy use.

Reinforcement Learning-Based Resource Allocation and Energy Efficiency Optimization for a Space–Air–Ground-Integrated Network

Reinforcement Learning-Based Resource Allocation and Energy Efficiency Optimization for a Space–Air–Ground-Integrated Network

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage

Tractor engine performance is critical for optimizing energy efficiency in tillage operations across various soil conditions. This study introduces an artificial neural network (ANN) based novel approach for predicting and optimizing specific fuel consumption (SFC) and fuel consumption per tilled area (FCA), during tillage operations. Field tests were conducted to collect the data on rear axle torque, forward velocity, fuel consumption, throttle position, engine speed, and depth of operation under soft, medium, and hard soil conditions while using a reversible MB plough, offset disc harrow, and rigid tyne cultivator. Tractor engine performance was predicted using ANN and optimized by matching the required rear axle power with the available rear axle power. Optimal operating parameters for three distinct tillage implements, each associated with varying soil types and tillage depths, were determined based on minimizing SFC and FCA. An integrated ANN-based user advisory system was developed to provide tractor engine performance predictions and recommendations for optimal operating parameters to maximize fuel efficiency. The recommendations were validated with actual field data, resulting in Mean Absolute Percentage Errors of 7.56% and 8.98% for MB plough, 11.19% and 9.36% for offset disc Harrow, and 10.90% and 10.57% for cultivator, respectively, for SFC and FCA. Highlights ANN-based novel approach is proposed to optimize fuel efficiency during tillage Advisory system is developed to recommend parameters for optimum tillage Suggestions for minimum SFC and FCA are provided in soft, medium and hard soil The accuracy and reliability of advisory system are proven by field validation

Deep fuzzy nets approach for energy efficiency optimization in smart grids

Using smart grids has become crucial for achieving efficient and sustainable energy management. One of the main challenges in smart grids is optimizing energy efficiency by managing and controlling electricity generation, transmission, and distribution. The Deep Fuzzy Nets (DFN) approach has been proposed as a novel technique that combines the capabilities of deep learning and fuzzy login to optimize energy efficiency in smart grids. The proposed approach utilizes a deep learning architecture to learn the complex relationships between various parameters within the smart grid system. The fuzzy logic component handles uncertainties and imprecision’s in the data, making the DFN approach well-suited for real-world energy management applications. The proposed approach can provide accurate and reliable predictions and enhancing energy efficiency in a dynamic and evolving smart grid environment. The proposed deep fuzzy nets approach reached 91% sensitivity, 94.45% specificity, 92/37% prevalence threshold, and 96.54% critical success index. This approach has been tested in various energy systems and has demonstrated capabilities to improve system-level energy efficiency while still giving users control of their energy usage. As energy efficiency optimization in intelligent grids continues to be a primary focus of energy research, deep fuzzy nets could provide a powerful solution for energy optimization.

A Meta-DDPG Algorithm for Energy and Spectral Efficiency Optimization in STAR-RIS-Aided SWIPT

A Meta-DDPG Algorithm for Energy and Spectral Efficiency Optimization in STAR-RIS-Aided SWIPT

Optimization of Energy Efficiency for Uplink mURLLC Over Multiple Cells Using Cooperative Multiagent Reinforcement Learning

Optimization of Energy Efficiency for Uplink mURLLC Over Multiple Cells Using Cooperative Multiagent Reinforcement Learning