Reduce Power Usage Research Articles

Impact of Ad Blockers on Computer Power Consumption while Web Browsing: A Comparative Analysis

This study explores the impact of various ad blockers on power consumption during web browsing, focusing on different types of online content. By analyzing power use across ten popular websites, the study assesses the performance of five widely utilized ad blockers: AdBlock, AdBlock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Power consumption was measured under controlled conditions, comparing scenarios with and without ad blockers to gain insight into their efficiency. The findings indicate substantial differences in power savings, with some ad blockers significantly reducing power usage, particularly on media-heavy sites, while others unexpectedly increased consumption under certain conditions. The study underscores the potential of ad blockers to enhance power efficiency in digital environments, highlighting the importance of optimizing ad-blocking techniques to reduce the environmental impact of online activities. Through comprehensive analysis and comparison, this research offers insights into selecting effective ad blockers to minimize power consumption, promoting more sustainable web browsing practices.

Research on Regulation Method of Variable-Air-Volume Air Conditioning System with “Personal Space”

In large public facilities, such as airport terminals or open-plan office spaces, the HVAC system typically consumes substantial amounts of energy. However, individuals often gather at specific areas while other zones are occupied by transient or occasional users. To minimize operational energy usage, this paper aims to reduce thermal comfort demands in non-targeted areas. This paper introduces a method for regulating the thermal environment around occupants exclusively in the variable-air-volume (VAV) air conditioning running mode. The investigation utilizes Airpak modeling and experimental verification techniques. Additionally, an analysis of temperature field and velocity field distributions within the room under the “personal space” operation mode is presented. The results suggest that adjusting the numbers of air vents, openings, airflow velocities, and air supply orientations can establish a comfortable thermal environment for inhabitants and reduce the overall ADPI value. The combined air supply mode leads to a 16.7% reduction in power usage compared to traditional full-space operation.

Task Scheduling Algorithm for Power Minimization in Low-Cost Disaster Monitoring System: A Heuristic Approach

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments.

Optimizing Power Consumption in Machining Nickel-Based Superalloys: Strategies for Energy Efficiency

<div>In the face of the world’s population growth and ensuing demands, the industrial sector assumes a crucial role in the management of limited energy supplies. Superalloys based on nickel, which are well-known for their remarkable mechanical qualities and resilience to corrosion, are now essential in vital applications like rocket engines, gas turbines, and aviation. However, these metals’ toughness presents a number of difficulties during machining operations, especially with regard to power consumption. This abstract explores the variables that affect power consumption during the machining of superalloys based on nickel in great detail and suggests ways to improve energy efficiency in this area. The effects of important variables on power consumption are extensively investigated, including cutting speed, feed rate, depth of cut, tool geometry, and cooling/lubrication techniques. A careful balance between these factors is necessary to maximize machining efficiency and reduce power usage. Furthermore, this study reviews the effect of different heat source applications on power consumption and the resultant quality of machined nickel-based superalloys. Additionally, the critical role of cooling and lubrication in mitigating the adverse effects of high temperatures generated during machining is thoroughly examined. Innovative cooling strategies, including cryogenic or high-pressure coolant systems, are explored as potential avenues to enhance heat dissipation and minimize power requirements. In essence, this abstract not only sheds light on the challenges inherent in machining nickel-based superalloys but also offers actionable insights into how energy efficiency can be maximized through strategic parameter optimization and the adoption of innovative cooling techniques. By addressing these aspects, manufacturers can effectively navigate the complexities of machining superalloys while minimizing their environmental footprint and operational costs.</div>

Effective incentive based demand response with voltage support capability via reinforcement learning based multi-agent framework

With the ongoing advancements in power grids, it is crucial to maintain a balance between energy generation and usage to ensure the stability of power systems. Any disparity between the supply and demand of energy can lead to increased expenses for utility companies and consumers, which might potentially result in a widespread failure of the grid. To tackle this problem, this study offers the implementation of the Voltage Capability Incentive-Based Demand Response (VC-IBDR) system, which utilizes a multi-agent framework. The main goal is to reduce power usage by providing financial incentives, while also ensuring that voltage standards are satisfied at each node. In this method, the aggregator agent coordinates demand response operations by interacting with household agents that are equipped with home energy management systems. Every household utilizes a disjunctively constrained knapsack problem optimization method to effectively organize the consumption of home appliances, considering customer preferences and dissatisfaction. During demand response events, the aggregator agent works together with household agents to optimize appliance schedules, ensuring that voltage levels at each node remain within acceptable levels. The simulation is performed to assess the efficacy of VC-IBDR on a feeder that provides service to 25 residential households. The findings indicate substantial decreases in power usage, with an average decline in demand of 4.20% at the feeder level and a 10.41% decrease in peak power consumption throughout the day. Furthermore, the voltage values at each node stay consistently steady within the range of 0.96 to 1.0 per unit throughout the day.

Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm

This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality.

Concentration of Microparticles/Cells Based on an Ultra-Fast Centrifuge Virtual Tunnel Driven by a Novel Lamb Wave Resonator Array.

The µTAS/LOC, a highly integrated microsystem, consolidates multiple bioanalytical functions within a single chip, enhancing efficiency and precision in bioanalysis and biomedical operations. Microfluidic centrifugation, a key component of LOC devices, enables rapid capture and enrichment of tiny objects in samples, improving sensitivity and accuracy of detection and diagnosis. However, microfluidic systems face challenges due to viscosity dominance and difficulty in vortex formation. Acoustic-based centrifugation, particularly those using surface acoustic waves (SAWs), have shown promise in applications such as particle concentration, separation, and droplet mixing. However, challenges include accurate droplet placement, energy loss from off-axis positioning, and limited energy transfer from low-frequency SAW resonators, restricting centrifugal speed and sample volume. In this work, we introduce a novel ring array composed of eight Lamb wave resonators (LWRs), forming an Ultra-Fast Centrifuge Tunnel (UFCT) in a microfluidic system. The UFCT eliminates secondary vortices, concentrating energy in the main vortex and maximizing acoustic-to-streaming energy conversion. It enables ultra-fast centrifugation with a larger liquid capacity (50 μL), reduced power usage (50 mW) that is one order of magnitude smaller than existing devices, and greater linear speed (62 mm/s), surpassing the limitations of prior methods. We demonstrate successful high-fold enrichment of 2 μm and 10 μm particles and explore the UFCT's potential in tissue engineering by encapsulating cells in a hydrogel-based micro-organ with a ring structure, which is of great significance for building more complex manipulation platforms for particles and cells in a bio-compatible and contactless manner.

Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.

Evaluation of the impacts of seawater integration to electrocoagulation for the removal of pollutants from textile wastewater

Recent textile industry expansion has a major environmental impact if not addressed. Being a water intensive industry, textile manufacturing is usually associated with wastewater management challenges. Electrocoagulation (EC) is recognized as one of the effective solutions to address these challenges. This study aims to investigate the potential of integrating seawater into the EC process for textile wastewater treatment, targeting optimal pollutant removal efficiencies. A simple electrolytic reactor was designed to investigate the removal efficiency of these treatments for chemical oxygen demand (COD), total suspended solids (TSS), turbidity, and color from textile wastewater at different seawater percentages and retention times. Notably, the addition of seawater not only improves the EC process efficiency but also significantly dilutes pollutants, reducing their concentrations. This dual effect enhances removal efficiency and dilution optimizes the treatment outcome. The highest removal efficiencies were achieved for COD (47.26%), TSS (99.52%), turbidity (99.30%), and color (98.19%). However, pH, dissolved oxygen (DO), and electrical conductivity increased with increasing retention times and seawater percentages in the EC process. Moreover, Seawater − EC integration reduces power usage to 15.769 Am−2 and costs approximately 0.20 USD/m3. To assess the effects of the retention times and seawater percentages on pollutant removal from textile wastewater, an analysis of variance (ANOVA) was conducted utilizing the Design-Expert 11 software. The best model obtained using Central Composite Design (CCD) was quadratic for COD (R2 = 0.9121), color (R2 = 0.9535), turbidity (R2 = 0.9525), and TSS (R2 = 0.9433). This study suggests that higher seawater percentages and longer retention times effectively eliminate contaminants but increase ion concentrations.

An efficient beaconing of bluetooth low energy by decision making algorithm

Ongoing research endeavors are exploring the potential of artificial intelligence to enhance the efficiency of wireless communication systems. Nevertheless, complex computational mechanisms, such as those inherent in neural networks, are not optimally suited for applications where the reduction of computational intricacy is of paramount importance. The rise in Bluetooth-enabled devices has led to the widespread adoption of Bluetooth Low Energy (BLE) in various IoT applications, primarily due to its low power consumption. For specific applications, such as lost and found tags which operate on small batteries, it’s especially important to further reduce power usage. With the objective of achieving low power consumption by optimally selecting channels and advertisement intervals, this paper introduces a parameter selection method derived from the Multi-Armed Bandit (MAB) algorithm, a technique known for addressing human decision-making challenges. In this study, we evaluate our proposed method using simulations in diverse environments. The outcomes indicate that, without compromising much on reliability, our approach can reduce power consumption by up to 40% based on the wireless surroundings. Additionally, when this method was implemented on an actual BLE device, it demonstrated effectiveness in reducing power consumption by about 35% in real environments.

Designing Power-Efficient BIST Architecture: Leveraging Reversible Logic for Scalable Digital Systems

This paper presents a novel approach to optimizing power usage in scalable Built-In Self-Test (BIST) controllers. While BIST mechanisms are crucial for maintaining the reliability of digital circuits, they can be excessively power-hungry during testing phases, particularly in applications where energy consumption is a concern. We propose an innovative architecture incorporating reversible logic gates and circuits to overcome this challenge. Reversible logic is renowned for its low power consumption as it retains information. By integrating reversible logic into our architecture, we can significantly reduce power usage during test cycles, making it an ideal solution for scalable systems ranging from 8 to 32 bits. Our trials showed substantial power savings compared to traditional BIST approaches without sacrificing test coverage or efficiency. Our research provides new opportunities to develop energy-efficient testing methods for digital circuits, contributing to broader efforts in sustainable electronics design.

Enhanced decision-making in healthcare cloud-edge networks using deep reinforcement and lion optimization algorithm

Addressing the limitations of traffic-centric approaches in cooperative cloud-edge networks, this paper introduces an adaptive deployment strategy for FDNN using LOA. However, the oversight of network structure hampers load-balancing efficiency in medical data categorization. To overcome this, a novel DRL-LOA approach integrates Deep Reinforcement Learning (DRL) with LOA, considering both network structure and traffic-related data. This DRL-LOA considers both network structure and traffic-related data for optimization tasks. The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. In light of this choice, the FDNN classifier is deployed to categorize the medical information. In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% % decrease in latency on 20 edge nodes in cloud-edge systems. These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks.

Data Center Energy Evaluation Tool Development and Analysis of Power Usage Effectiveness with Different Economizer Types in Various Climate Zones

Data centers are energy-intensive facilities, with over 95% of their total cooling load attributed to the heat generated by information technology equipment (ITE). Various energy-saving techniques have been employed to enhance data center efficiency and to reduce power usage effectiveness (PUE). Among these, economizers using outdoor air for cooling are the most effective for addressing year-round cooling demands. Despite the simplicity of the load composition, analyzing data center cooling systems involves dynamic considerations, such as weather conditions, system conditions, and economizer control. A PUE interpretation tool was specifically developed for use in data centers, aimed at addressing the simplicity of data center loads and the complexity of system analysis. The tool was verified through a comparison with results from DesignBuilder implementing the EnergyPlus algorithm. Using the developed tool, a comparative analysis of economizer strategies based on the PUE distribution was conducted, with the aim of reducing the PUE of data centers across various climatic zones. The inclusion of evaporative cooling (EC) further improved cooling efficiency, leading to reductions in PUE by approximately 0.02 to 0.05 in dry zones. Additionally, wet zones exhibited PUE reductions, ranging from approximately 0.03 to 0.07, with the implementation of indirect air-side economizer (IASE). Sensitivity and uncertainty analysis were further conducted. The computer room air handler (CRAH) supply temperature and CRAH temperature difference were the most influential factors affecting the annual PUE. For the direct air-side economizer (DASE) and DASE + EC systems, higher PUE uncertainty was observed in zones 1B, 3B, 4B, and 5B, showing ranges of 1.17–1.39 and 1.15–1.17, respectively. In the case of the IASE and IASE + EC systems, higher PUE uncertainty was noted in zones 0A, 0B, 1A, 1B, and 2A, with ranges of 1.22–1.43 and 1.17–1.43, respectively. The distinctive innovation of the tool developed in this study is characterized by its integration of specific features unique to data centers. It streamlines the computation of cooling loads, thus minimizing the burden of input, and delivers energy consumption data for data center cooling systems with a level of precision comparable to that of commercial dynamic energy analysis tools. It provides data center engineers with a valuable resource to identify optimal alternatives and system design conditions for data centers. This empowers them to make informed decisions based on energy efficiency enhancements, thereby strengthening their ability to improve energy efficiency.

Design Guidelines for Low Power Embedded Systems using Low Power Electronics

The design of Low Power Embedded Systems (LPES) and IoT products may include a variety of power management techniques, or they may incorporate sophisticated on-chip capabilities that assist in reduced power usage. The power management and low power consumption of embedded systems are also enabled by complex algorithms, and every Low Power System (LPS) may require a mix of approaches to avoid using extra battery power. There are several strategies we may use when creating an LPES that must be extremely power efficient while simultaneously offering the necessary degree of computational capability. It all depends on the design specifications that must be met. Then, if at all feasible, choose the appropriate low-power components from Low Power Electronics (LPE). After studying and reviewing multiple LPES real-time projects, we compiled a list of a few strategies we may use to approach low-power design and consumption for embedded systems. Following the analysis of numerous LPES real-time projects, we came up with a list of a few approaches to low-power design for embedded systems using LPE. There are additional benefits of LPES design. Less heat is generated through LPES, which is better for the environment. For 1000 LPES devices, one watt of power saved per device equals one KWH i.e. we can save 1 unit of electricity. Design with low power increases the component and system reliability. The embedded system's operating life is enhanced. In many instances, LPES designs may result in a reduction in production costs. The LPE components chosen are more affordable and inexpensive. Hence, the low-wattage power supply, LPES design is easier and less expensive.

A Reliable Network Routing Protocol Design with an Intelligent Mobile Sink and Energy Efficiency over Wireless Sensor Network

This is crucial for the successful operation of a Wireless Sensor Network, frequently called as WSN as each of the sensor nodes in the network's structure are in charge of transmitting the information from its original source to its final destination. Several researchers have developed a Mobile sink in an effort to enhance transmission quality. Although it has been shown to be beneficial, the network's overall reliability is compromised by the much more energy-intensive operation of the nodes. In this research, we offer a routing strategy that makes use of cluster and source portability to drastically reduce power usage. We have given this protocol its name: the Intelligent Mobile Sink Assisted Routing Protocol (IMSARP). To get started, we divide an entire sensor environment into regions, and within each of them, members cast proportional votes to choose who will serve as the Cluster-Head (CH). To determine how to choose the most efficient choice, nodes in the network evaluate the power consumption of all feasible routes. The proposed IMSARP method uses a cluster-based paradigm to construct a mobile-sink routing protocol. The standard quantity of energy within all the clusters serves as what drives the sink's motion. The outcomes section of this research proves the legitimacy and maintains the credibility of the proposed system by presenting the results, which include throughput, delay reduction, energy efficiency, and data transfer rate.

VSI Controller Integrated DC-DC Converter for Electrical Vehicle Charging System in PV Solar Grid

With the increased use of electric vehicle (EV) vehicles, the deployment of EV charging stations has also increased. Various topologies are adopted to deploy EV charging stations to use available energy efficiently. In most EV charging stations, photovoltaic (PV) systems are integrated to reduce power usage from the main source and utilize the naturally available energy. Mostly, the PV systems are integrated into the EV-charging stations through grid control which reduces the power consumption and requirement to a significant extent. However, these systems can be further optimized by implementing DC-DC converters and voltage source inverter (VSI) controllers, which improves power usage efficiency and helps integrate PV systems into the EV charging stations. This paper proposes an efficient EV charging station model charged through both PV and primary source through DC-DC converter and VSI controllers for intelligent utilization of the available energy. The proposed system is compared with an existing system, and the results are verified. Matlab is used to simulate it, and the findings are well discussed. The outcome show that the proposed model makes efficient use of the available energy with a steady current and voltage output and minimum total harmonic distortions in the system.

An optimal train journey with bounds on energy consumption during specified intermediate time intervals

In order to mitigate consumption of electrical energy during periods of peak demand it is common for electricity system operators to offer financial incentives that encourage large corporate consumers to reduce power usage at designated critical times. For train operators on large rail networks it may be more profitable to limit energy consumption during these times, for selected individual train journeys, rather than always implementing a true optimal strategy that minimizes overall energy consumption. In this paper we use classical methods of constrained optimization to find driving strategies for a single train that minimize energy consumption subject to constraints on energy usage within designated high-demand time intervals during the journey. We illustrate our results by considering a journey with realistic parameters and two different nominal sets of energy consumption constraints.

A multi-sensory real-time data transmission method with sustainable and robust 5G energy signals for smart cities

New generation technologies like Fifth Generation (5G) access technology have great potential to provide a sustainable Quality of Service (QoS) for Electric Vehicles (EVs) in smart cities. The environmental features of urban cities mean that signal transmission can be affected across communication networks, with packet delays, packet loss and higher error rates occurring during data exchange. There is a need for sustainable and robust low-energy signals for smart EV communication. The present paper proposes a novel method for continuous, robust video data transmission at low energy via multi-sensory-enabled 5G technology. The transceivers (transmitters and receivers) are designed with multiple low-energy sensors for detecting data availability at the receiver device. By applying a threshold level detection technique, data availability is identified dependent upon energy level and successive data exchange occurs between transceivers. The application of low-energy signal transmission reduces power usage during video data transmission and leads to a sustainable solution for intelligent EV applications. The performance of the proposed multi-sensory technique is tested and verified using various calibrated parameters considering key metrics including collision error, propagation error, sensing error and accuracy. The proposed model significantly improves accuracy, with higher sensing capabilities and lower delays and propagation errors compared to the existing buffer model.

Annual Effect of the VRF Control Algorithm in Response to the TOU Rate Plan

Many countries adopt a time-of-use (TOU) rate system, in which electricity rates vary by season and time of day, to reduce power usage during peak power consumption hours. South Korea offers a TOU rate plan that depends on the electricity usage of a building and its contracted power; in this plan, the electricity rate reaches up to 300% depending on the time of day. Hence, electrically powered variable refrigerant flow (VRF) systems are increasingly being installed in small- and medium-sized buildings requiring individual cooling and heating operations. This study aims to develop a new control algorithm to reduce electricity consumption and electricity rates for cooling and heating by VRF systems in university buildings adopting the TOU rate plan and apply it to actual buildings to verify the reduction effect. The proposed control algorithm primarily consists of a module that controls the refrigerant evaporation temperature (cooling) and high pressure (heating) according to the indoor heat load and a module that controls the indoor set temperature based on the hourly electricity rate. The developed algorithm was installed in the controller of a VRF system installed in an actual university building and the annual effect was verified using the method proposed by the International Performance Measurement and Verification Protocol. As a result, power consumption was reduced by 17.8% for heating and 4.0% for cooling due to the application of the control algorithm, and the electricity rates reduced by 19.2% and 7.3%, respectively.

Augmented digital twin for railway systems

The information that automated train control (ATO) systems use to improve safety and reduce power usage is limited by on-board and wayside monitoring applications and computing power. This paper presents an augmented digital twin for railway applications that enables real-time consideration of derailment risk in train operations. The augmented digital twin implements a surrogate model with the results of a massive multibody dynamics numerical program and machine learning models to predict the instantaneous wagon derailment risk. A case study for a heavy haul iron ore wagon with three-piece bogies was conducted to test the augmented digital twin. A multibody simulation numerical program comprising 2100 simulation cases was completed. The surrogate model was developed using linear, polynomial, decision tree and ensemble forest regression models on the results of the numerical program. A longitudinal train simulator was used to calculate the speed and lateral coupler force throughout a train trip. The surrogate model effectively predicted the derailment index for empty and loaded conditions accounting for lateral coupler forces, vehicle speeds and curve radius. The proposed augmented digital twin can be further developed to accomplish other train operational benefits such as the reduction of rail damage.