Power Usage Research Articles

Biomolecular Neuristors from Functionalized Lipid Membranes

AbstractModeled after biological neurons, neuristors are emerging hardware that generate recurring voltage spikes in response to electrical stimulation. This type of excitability can enable transistor‐free spiking neural networks for efficient signal processing and computing. Yet, most neuristors consist of circuits containing numerous devices, thus complicating fabrication and increasing size, power usage, and cost. In contrast, it is shown that a single, 5nm‐thick lipid membrane functionalized with voltage‐activated peptides functions as a two‐terminal, ultra‐low power (fW‐pW) artificial neuristor in response to supplied current. Specifically, the biomolecular membrane generates stochastic voltage oscillations (10–150 mV) in response to direct currents (|5–40| pA), and is capable of generating two distinct types of action potentials fast (≈1–50 ms) and slow (≈1–2 s) spikes via distinct physical mechanisms. This discovery showcases the inherent multifunctionality and modularity of engineered biomembranes, and it contributes to an expanding suite of ionic and biomolecular devices designed with synapse and neuron functionalities for emerging computing architectures.

Exploring the potential of 5G uplink communication: Synergistic integration of joint power control, user grouping, and multi-learning Grey Wolf Optimizer

Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

Size matters: the influence of supplier size on buyer's usage of mediated power in positive and negative supplier-induced disruptions

Size matters: the influence of supplier size on buyer's usage of mediated power in positive and negative supplier-induced disruptions

Advancements in IoT Routing and Energy Efficiency: A Comprehensive Review of Algorithms and Technologies

The rapid expansion of the Internet of Things (IoT) has led to a significant increase in connected devices, resulting in growing challenges related to efficient data transmission, energy consumption, and network scalability. Routing protocols and energy-efficient algorithms play a pivotal role in addressing these challenges, enabling IoT systems to optimize performance while minimizing power usage. This review provides a comprehensive analysis of recent advancements in IoT routing techniques and energy-efficient solutions, focusing on multi-objective optimization algorithms such as fractional gravitational search, grey wolf optimization, and hybrid salp swarm-differential evolution algorithms. Additionally, we explore context-aware routing, cluster-based methods, and the integration of machine learning to enhance decision-making in dynamic IoT environments. The review also highlights the critical role of energy-efficient routing in IoT applications, such as smart agriculture, healthcare, and smart cities, emphasizing its importance in prolonging network lifetime and reducing overall operational costs. The potential of blockchain technology and AI-driven algorithms to enhance security and energy efficiency in IoT networks is discussed, alongside future directions in green routing solutions. By addressing the complexities of IoT routing and energy management, this review aims to provide insights into the latest research trends and guide future innovations in IoT systems.

Impacts of Renewable Energy Transitions on the Economic Growth in Malaysia: A Dynamic ARDL Simulations

This study explores the dynamic relationships between renewable energy transitions and economic growth in Malaysia, with a specific focus on hydroelectric power, using the Dynamic Autoregressive Distributed Lag (D-ARDL) simulations. The research leverages extensive time-series data from 1988 to 2022 to analyze the impacts of hydroelectric and non-renewable energy consumption on the nation's economic trajectory. Our findings indicate a positive long-term relationship between hydroelectric power usage and GDP growth, highlighting renewable energy's pivotal role in sustainable economic development. Additionally, the study examines the short-term adjustments and the effectiveness of hydroelectric power in reducing dependence on non-renewable energy sources, which are crucial for achieving Malaysia's Nationally Determined Contributions (NDC) for 2030. The dynamic ARDL simulations provide nuanced insights into the potential future impacts of increased hydroelectric power usage, underscoring its dual benefits for economic growth and environmental sustainability. This research contributes to the understanding of energy transitions within the context of Malaysia's unique economic and environmental landscape, offering implications for policy and investment in renewable energy infrastructure.

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two‐dimensional (2D) materials have been extensively investigated as high‐performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α’‐4H‐borophene sheets and metal β12‐borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non‐volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene‐based memories in information storage and in‐memory computing.

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets.

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two-dimensional (2D) materials have been extensively investigated as high-performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α'-4H-borophene sheets and metal β12-borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non-volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene-based memories in information storage and in-memory computing.

Enhanced you only look once approach for automatic phytoplankton identification

<p>Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YOLO compared to the YOLOv8m model, further demonstrated by the decreased giga floating-point operations (GFLOP), decreased parameter count, and compact dimensions, significantly improves the automation of phytoplankton species identification. This suggests that the ACVIT-YOLO model could produce a better prediction system for identifying phytoplankton with similar accuracy to the original YOLOv8m model but with lower computational power and resource usage.</p>

Joint Power Allocation and User Fairness Optimization for Reinforcement Learning Over mmWave-NOMA Heterogeneous Networks

Joint Power Allocation and User Fairness Optimization for Reinforcement Learning Over mmWave-NOMA Heterogeneous Networks

Enhancing Conveyor Belt Performance: Evaluating the Impact of In-creased Capacity Using Belt Analyst Software

This study investigates the effects of increasing conveyor belt capacity from 148.5 tons per hour (t/h) to 180 t/h on the overall system performance, employing both manual measurements and simulations using Belt Analyst software. The research aims to evaluate critical parameters such as effective pulling force, motor power requirements, structural load, and belt deflection, which are essential for determining the feasibility and impact of such an upgrade. The analysis reveals that with the capacity increase, the effective pulling force required rises to 14,072 N, while the motor power usage escalates to 15 kW. Concurrently, the structural load experiences a significant increase from 46.144 kg/m to 56.238 kg/m, and belt deflection intensifies from 22 mm to 27 mm. These findings suggest that increasing the conveyor belt capacity to 180 t/h, may lead to increased stress on the structure and belt, which could potentially affect the lifespan and performance of the conveyor system. Furthermore, while the conveyor system's performance enhances at the higher capacity, it also places additional stress on the system's components. The study further examines the implications of these changes, emphasizing the potential risks to the conveyor belt’s structural integrity and the possible reduction in its lifespan due to the increased mechanical stress. It is highlighted that careful consideration and precise engineering adjustments are necessary when planning capacity enhancements to avoid adverse effects on the system's longevity and reliability.

Electricity theft detection model based on the CEEMDAN-CNN-ViT

Abstract Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods.

Machine learning accelerated the performance analysis on PCM-liquid coupled battery thermal management system

With the increasing prominence of thermal management issues in lithium-ion batteries, the performance of active-passive hybrid battery thermal management systems attracts extensive attention. In this study, a novel phase change material-liquid cooling coupled battery thermal management system was designed. The effects of battery arrangement structure, liquid flow direction, mass flow rate, startup temperature, phase change material melting point, and liquid cooling power consumption on the thermal management performance of the battery module during high-rate discharge were numerically investigated. The results show that the phase change material-liquid cooling system can effectively regulates battery temperature within safe limits under optimal operational conditions. Compared to the module without liquid cooling with an initial temperature of 40 °C, the maximum temperature is reduced by 15.18 °C and 27.5 °C during 3C and 5C discharge, respectively. Lower liquid cooling startup temperature and higher liquid flow rate contribute further to reducing the maximum module temperature, albeit with increased energy consumption. Compared to continuously operating liquid cooling at 0.01 kg/s, adjusting liquid cooling operating point with minimized power usage can drastically reduce energy consumption by up to 94.71 % during 5C discharge and ambient temperature conditions. Additionally, machine learning with three multi-objective regression prediction models were utilized to predict the temperature and energy consumption of the proposed coupled system. The error between the Random Forest model's predicted values and the simulation results was determined to be <5 %.

Energy performance analysis of multi-chiller cooling systems for data centers concerning progressive loading throughout the lifecycle under typical climates

AbstractThe increasing demand for cooling energy in data centers has become a global concern. Existing studies lack a comprehensive analysis of the energy performance of widely used multi-chiller cooling systems in air-cooled data centers throughout their lifecycle, especially concerning progressive loading. To bridge this gap, this study conducts a thorough assessment of the energy performance of multi-chiller cooling systems throughout the entire lifecycle. Additionally, the impact of climate conditions on the energy efficiency of the cooling systems is analyzed, considering design variations for typical climates. Multi-chiller cooling system models are developed using the test data of cooling equipment and typical control algorithms. The energy performance of the cooling system is thoroughly analyzed under full-range cooling loads and climate conditions. Results show that free cooling time could differ up to 1442 hours at different part load ratios in the same location. Furthermore, the cooling system’s coefficient of performance (COP) varies significantly, by up to 6, at different part load ratios, corresponding to a difference in power usage effectiveness (PUE) up to 0.14. Notably, the average cooling system COP throughout the lifecycle loading is found to be only 11.7, 2.9 lower than the design system COP.

SCADA Implementation on Solar Panel Electricity Consumption Monitoring and Regulation System

In an effort to motivate students and understand application of SCADA to the Solar Panel system which is one source potential non - fossil energy to be used as solution to meeting sustainable energy needs.​ Where in its operation , efficiency and reliability Solar Panel system is still a major problemthat must be overcome . Utilization of Supervisory Control and Data Acquisition (SCADA) in management Solar Panel Systems (PLTS) have become significant solution​ to improve loading efficiency . The purpose of this research is to create a prototypes solar panel practicum as a learning media to motivate student in understanding how solar panels work and can apply the process of monitoring and regulating solar panel system with SCADA in real-time. The equipment used​ includes Solar Panels, Solar Charger Controller (SCC), Inverter, Battery , PLC Omron CP1E-NA20DR-A and CP1W AD041, ACS712 Current Sensor , ZMPT101B Voltage Sensor, DHT22 Temperature and Humidity Sensor and Light Sensor (LDR) as well as CX- Programmer software Ver. 9.5 and CX -Supervidor Ver.4.0. The method used is Research and Development method as the basic for design and manufacture of solar panel practicum tools and the application of SCADA to the monitoring system and electricity usage of solar panels . From the results of tests conducted with the application of SCADA on solar panels can monitor Voltage , Current , Power, Temperature , Light and load usage regulator electric solar panels based on electrical energy consumption, as well as the condition of electrical equipment connected to the solar panel system with an average error values : Voltage 0.084%, Current 0.155%, Power 0.107% and Temperature 45 0C and Light 658.7 Lux.

Local Load Migration in High-Capacity Fog Computing

Fog computing brings storage and computational capabilities closer to the data source, which reduces latency and enhances efficiency in processing data. However, these capabilities are resource-constrained at the fog nodes as compared to the cloud core. This limitation can result in high computational loads that yield in saturation and congestion at the fog nodes when processing large traffic volumes. Hence, this paper introduces a novel load migration scheme for delay-sensitive and computation-intensive requests in fog computing without relaying to the cloud core, thus avoiding prolonged link delays. First, a grouped service function chain (SFC) provisioning framework is embedded on a heterogeneous architecture composed of super fog (SF) and ordinary fog (OF) nodes, where all the virtual network functions (VNFs) in the SFC are mapped on a single SF node to reduce resource use. Here, the SF nodes are always in active mode to receive incoming traffic, whereas the OF nodes are maintained in idle mode to save power and computing resources. When the SF node approaches a saturation threshold alarm, it diffuses its load (hosted VNFs) gradually to neighboring OF nodes in the vicinity, thus avoiding saturation at the SF node and providing service continuity. The framework is implemented on a resource-constrained network to achieve realistic operating conditions. Overall, the proposed work achieves high admission and resource utilization rates, reduced delays, and power consumption, as compared to key network architectures and provisioning schemes that separately map delay-sensitive and computation-intensive on hierarchical fog layers, which incur increased delays, network traffic, and power usage.

IoT based Smart Electricity Monitoring and Control Using ESP-Mesh Networking

The "IoT-Based Smart Electricity Monitoring and Control System Using ESP-MESH Networking" is a state-of- the-art project that aims to transform the way we manage and consume electricity in our homes. In today's world, our homes are equipped with a wide range of electrical appliances, each with varying levels of power consumption. Without an effective system to monitor and control these appliances, they can lead to excessive electricity usage. This not only results in high energy bills but also contributes to a significant carbon footprint, which is harmful to our environment. The Power consumption by the overall house can be monitored using this device and control the other electronic devices, able to monitor the power consumption in different rooms and able to monitor the current power by specific devices. User can check their Power usage from anywhere and at any time interval. The information can be monitored from mobile applications and also be monitored even on a web page. The Main can be turned on/off using a relay from anywhere. The Home application can also be controlled from anywhere through the mobile application. Keywords— ESP-MESH Networking, Power Consumption, Energy efficiency, Carbon footprint, Relay control, Energy management

Comprehensive assessment of waste heat recovery mismatch and renewable energy integration in data centers: A multifaceted energy, economic, and environmental perspectives

The integrated utilization of free cooling sources, waste heat recovery from information technology equipment, and renewable energy emerges as a pivotal method for conserving energy and mitigating carbon emissions in data centers. However, there is limited research on systems that integrate these three technologies. A notable mismatch arises between the waste heat generated and the requirements for building heating, but it is often overlooked. Moreover, a discernible lack of comprehensive analysis concerning the multifaceted influences associated with using renewable energy exists. Therefore, this paper employs a range of metrics to comprehensively assess a renewable energy integration system considering the mismatch of the waste heat recovery in data centers. The results indicate that due to mismatch characteristics, the energy-saving rate of heating systems will not constantly climb with the increase in energy savings. It causes only 45.5% of waste heat available for heating purposes achieving a power usage effectiveness of 1.17 and the rest will be abandoned. Similarly, its economic performance doesn't absolutely improve with the expansion in heating volume, the payback period doesn't continue to fall but increases after reaching the optimal level of 5.7. The proposed system accomplishes a 45.8% reduction in energy consumption for the cooling system, a notable decrease of 90% in carbon emissions, and a 55.7% reduction in annual cash flow. It is also important to note that while integrating battery systems aids in stabilizing grid disturbances, with a minimum disturbance index of 0.2, it introduces significant economic costs for the data centers.

Optimizing plasma-activated water for enhanced microbial control in agricultural produce processing

The study utilized a pinhole plasma jet discharge system to generate plasma-activated water (PAW) for the removal of microbes on bird's eye chili. Optimal conditions for PAW generation were determined using response surface methodology, achieving maximum microbial inactivation. The novelty of this study lies in designing a PAW system model for agricultural cooperatives using the Quality Function Deployment (QFD) methodology, which integrates customer and engineer/designer perspectives. The results demonstrate a maximum 82.54% microbial removal rate on bird's eye chili under optimal conditions (e.g., exposure time = 20 min, Ar gas flow rate = 5 L/min, and O2 mixture = 2% Ar gas), indicating effective antimicrobial activity of plasma-activated water. Furthermore, a model of the PAW system for agricultural cooperatives, including cost and electrical power usage estimates, is proposed based on the correlation matrix of QFD. This conceptual PAW model for agricultural cooperatives warrants further investigation to assess its consistency in sterilization efficiency and long-term effects on treated agricultural products.

An IoT Based Smart Watt Metering for Energy Management System

This paper presents the analysis of IoT based Smart Watt Metering for Energy Management System. The proposed system, "An IoT Based Smart Watt Metering for Energy Management System," introduces a cost-effective and energy-efficient solution for monitoring and managing power consumption of electrical appliances. Utilizing non-intrusive current and voltage sensors, the system measures key electrical parameters such as current, voltage, and power factor in real-time. The ESP32 microcontroller, known for its low power consumption, processes and transmits this data via Wi-Fi to a central server or cloud platform, enabling remote access through a user-friendly web or mobile application. The interface allows users to visualize power usage trends, set consumption thresholds, and receive alerts for abnormal usage. Additionally, the system incorporates data logging capabilities, storing historical consumption data for long-term analysis and energy-saving decision-making. This innovative approach provides a detailed and accessible means for users to optimize energy usage and reduce costs, contributing to more efficient energy management amidst the global energy crisis

A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building

Accurate machine learning (ML) predictions for the early stages of the building design are crucial to construct energy-efficient buildings utilizing limited resources. Several studies have employed ML methods for energy consumption (EC) prediction without considering the utmost crucial PCM-integrated building design parameters. In addition, reducing the dataset by considering only the most significant building design parameters before applying the ML-based method would be beneficial for reducing the computational power and memory usage of the system as well as utilizing less time in the modelling process. To this end, this research presents a novel framework to establish the most robust and reliable ML-based prediction model with less complexity, considering only the most influential PCM-integrated building design parameters. These parameters were identified for future scenarios of hot semi-arid (BSh) climate zones using multi-stage sensitivity analysis. Afterward, a reduced EC database based on the most significant building's early-design-stage parameters (EDSPs) was utilized to formulate several multi-expression programming (MEP) and support vector machines (SVM)-based forecasting models, considering the variations in their hyperparameter values. Formulated prediction models have shown less time utilization through the training and testing phases for the EC evaluations of selected PCM-integrated building compared to the physical-modelling process. Several statistical parameters were used to test and validate the performance of the formulated prediction models. The acquired model evaluation and validation results demonstrated that the MEP-based prediction model (MEP15) exhibited the highest level of reliability and accuracy, showing an R2 value of >95% for both the training and testing phases. The model's interpretability showed that, throughout the parametric analysis, the developed prediction model adhered to the system's physical boundary constraints. Also, the best-performing prediction model showed energy savings of up to 12% for building integrated with PCM having a melting temperature of 28 °C. Conclusively, this research demonstrated that the developed MEP-based prediction model could be employed to precisely forecast the EC for selected PCM-incorporated building in the whole BSh climate, considering important EDSPs while utilizing minimal resources.