Affect Power Consumption Research Articles

A Developer-Oriented Framework for Assessing Power Consumption in Mobile Applications: Android Energy Smells Case Study

Currently, people spend a lot of time using their mobile devices. With such ubiquity of mobile devices in our daily life, battery capacity and quality are of utmost importance. Running software applications (called apps) are one of the major factors influencing the power consumption in mobile devices. In order to meet user needs, mobile apps are becoming inherently complex and resource greedy. Therefore, fulfilling these requirements at the pace imposed by the market may degrade software construction quality and insert so-called energy code smells: bad patterns in the source code of an app that indicate a deeper problem and adversely affect power consumption. This work proposes a developer-oriented framework for identifying and fixing patterns via analyzing different application code flavors in a user-driven test scenario. A special app was designed in order to validate the Android implementation of the proposed methodology. The study results have shown significant improvement regarding energy efficiency after correcting one or more energy code smells, with a 4 to 30 percent decrease in battery drain. Additionally, the power consumption signature term is defined in the context of mobile applications. This paper presents a developer-oriented framework for assessing power consumption in mobile applications. Our key contributions include identifying significant energy code smells, demonstrating their impact on power consumption, and providing a toolset for developers to improve energy efficiency.

Sustainable hard machining under zirconia nano-cutting fluid: A step towards a green and cleaner manufacturing process

The present study deals with the machinability (flank wear, surface integrity, cutting temperature and power consumption) and sustainability (Carbon emission and noise emission) investigations during hard machining under dual nozzle assisted novel ZrO2 based nano fluid MQL condition considering Taguchi L27 OA design of experiment. 0.3 % wt. ZrO2 nanofluid MQL medium effectively reduce cutting temperature and found as 65 °C, which shows the effective cooling and lubrication properties of zirconia nanofluid. Improvement in surface quality has been observed with lower rate of flank wear. Depth of cut and feed are found to be the most influential factor affecting power consumption and carbon emission with contribution of 47.213 % and 60.861 % respectively. Noise emission mostly affected by the depth of cut with a contribution of 43.40 %. Optimal parametric condition yields d: 0.1 mm, f: 0.1 mm/rev and v: 80 m/min through WASPAS and sustainable towards manufacturing industries for cleaner hard machining.

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>

Electrochemical oscillation behaviors of gold during its recovery from waste printed circuit boards through slurry electrolysis

Slurry electrolysis is an effective technology for recovering gold from waste printed circuit boards (WPCBs). However, electrochemical oscillation during the gold recovery process is a potential factor affecting power consumption. As a result, the potential oscillation behaviors of gold dissolution at a constant current were studied. These results showed that the periodic formation and dissolution of the Au-OH passive film on the anode induced electrochemical oscillation. The amplitude and frequency of electrochemical oscillation were affected by hydrochloric acid concentration, current intensity, electrode spacing, and sodium chloride concentration, among which the concentration of sodium chloride had a significant inhibitory effect. Under the following conditions: hydrochloric acid concentration of 8 mol/L, current of 0.03 A, and electrode spacing of 1.5 cm, the obtained maximum amplitude potential and frequency were 1.17 V and 0.56 Hz, respectively. The phase space reconstruction results showed that the systems evolved between the near-linear equilibrium region and the periodic oscillation region. Different factors induced two oscillation modes: top-down and bottom-up. The top-down and bottom-up oscillations induced by current intensity, hydrochloric acid concentration, and electrode spacing reduced the relative power consumption by 11.8, 17.2, and 11.8 %, respectively, and increased the relative power consumption by 14.3, 21.2, and 30.1 %, respectively. A critical value was obtained, which varied oscillation patterns and gained a region of lower power consumption. The addition of trisodium citrate and PVP can improve gold recovery rate and current efficiency by regulating process of electrochemical oscillation. This work will provide theoretical support for recovering valuable metals from WPCBs with reduced energy consumption by controlling the external conditions to switch the oscillation mode.

Optimizing Electric Vehicle Performance, Range and Parameter Estimation through NEDC Urban and Suburban Analysis using MATLAB

This research paper focuses on electric vehicle (EV) driving range and parameter estimation, with an emphasis on analyzing the New European Driving Cycle (NEDC) urban and suburban drive cycles. By examining these drive cycles, we obtain critical data on the moments when an electric vehicle transitions between constant velocity, acceleration, and deceleration phases, which significantly affect power consumption and driving range. We investigate various techniques for parameter estimation, including battery capacity, energy consumption rates, and powertrain efficiency, essential for improving EV performance and providing realistic range expectations. Empirical experiments in diverse driving conditions contribute valuable data to refine our understanding of EV driving range and performance in different scenarios. The research underscores the role of predictive modeling, data analytics, and advanced technologies in real-time parameter estimation, offering precise and convenient range predictions to enhance user confidence.

Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI)

Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.

Does the green finance development and renewable energy affect the economic recovery in Asian economies

This study examines how renewable energy and green finance affect energy consumption in Asian economies by analysing how they affect power consumption in both unpredictable and asymmetrical ways. We use the NARDL and multiple linear regression algorithms to gather information from 1986 to 2018. The findings show that China (0.65), India (0.57), Pakistan (0.54), Bangladesh (0.48), Indonesia (0.37), and Russia (0.38) have the greatest variation in green financing growth in Asia (0.36). Russia had the highest number of persons per capita in 2020 (0.55), followed by China. According to empirical investigations, the biggest obstacle to green energy effectiveness is the shortage of corporate and public sector investments in the energy market to strengthen supply security, enhance electricity availability, and encourage ecologically friendly productivity expansion. The results show that the implementation of renewable energy and energy conservation in Asian countries has a bright but hazardous potential.

Investigation on the Performance of Coated Carbide Tool during Dry Turning of AISI 4340 Alloy Steel.

The machinability of materials is highly affected by their hardness, and it affects power consumption, cutting tool life as well as surface quality while machining the component. This work deals with machining of annealed AISI 4340 alloy steel using a coated carbide tool under a dry environment. The microhardness of annealed and non-annealed workpieces was compared and a significant reduction was found in the microhardness of annealed samples. Microstructure examination of the annealed sample revealed the formation of coarse pearlite which indicated a reduction of hardness and improved ductility. A commercially CVD multilayer (TiN/TiCN/Al2O3/ZrCN) coated cemented carbide cutting tool was employed for turning quenched and tempered structural AISI 4340 alloy steel by varying machining speed, rate of feed, and depth of cut to evaluate the surface quality, machining forces, flank wear, and chip morphology. According to the findings of experiments, the feed rate possesses a high impact on surface finish, followed by cutting speed. The prominent shape of the serrated saw tooth chip was noticed at a higher cutting speed. Machined surface finish and cutting forces during turning is a function of the wear profile of the coated carbide insert. This study proves that annealing is a low-cost and economical process to enhance the machinability of alloy steel.

Increasing Consumer Drone Safety Through an IoT-Based Custom Illumination System

Consumer-based drone applications have been increasing rapidly in recent years. Most of these applications, such as aerial surveillance, crop dusting, and real estate photography rely, on frameworks that are based on the Internet of Things (IoT). Although solutions have been proposed to security issues due to connectivity, physical safety issues have not been sufficiently discussed. To solve one such issue, the conspicuousness of drones during flight, we have proposed an illumination system that can adapt to different weather conditions using an IoT framework. To test the feasibility of our illumination system, we conducted two tests, a power consumption test, and a human-in-the-loop assessment. The power test showed different illumination configurations did affect power consumption. The human-in-the-loop assessment demonstrated the effect of illumination on distance perception and how sunlight levels affected the drone's preferred illumination color. As drones begin to operate in more crowded spaces, such as cities, the need to ensure the safety of the public will be paramount. Through this prototype, we aim to provide designers with guidance on how to consider illumination for safer drone operations. Future studies, we plan to explore using illumination to increase safety and congruence in future application areas, such as delivery.

Analysis of the major factors that effect power consumption: A multiple regression analysis approach

The erratic power supply in recent times has been a matter of great concern not only to the consumers but also to the Power Holding Company of Nigeria (PHCN) organization itself. The nagging problem is compounded by the spasmodic nature of the phenomenon and hence calls for a special scientific investigation. The paper uses a multiple regression analysis technique to investigate the major factors that affect power Consumption in Jos Metropolis – traditionally tin-mining Camp. On the initial assumption that erratic power supply can only be due to the disparity between supply and demand. The paper sets out to answer the following two questions; i) what factors have been responsible for such increase in demand and the behaviour of the relationship that exist between these factors; ii) what likely measures are to be taken to restore the balance between demand and supply. We were able to identify the major factors as Residential Industrial and Commercial. Some interesting relationships were established.

Real-World Evaluation of Power Consumption and Performance of NB-IoT in Malaysia

Narrowband Internet of Things (NB-IoT) is expected to lead the way in wireless access technologies and support major 5G-based Internet of Things applications in the future. Since NB-IoT has yet to be fully deployed globally, there are only limited real-world performance evaluations available, particularly in the case of outdoor performance. Therefore, this study expanded on previous studies and comprehensively evaluated the performance of NB-IoT in the real world in terms of coverage parameters, path loss (PL), packet delivery rate (PDR), and latency limits. Numerical and detailed analyses were also performed to calculate power consumption and estimate the average battery life of NB-IoT, Sigfox, and LoRaWAN in relation to the various factors affecting power consumption. These three technologies were then compared in terms of power consumption. An overall PDR of 91.76% was achieved, indicating that NB-IoT can handle high data rates with minimal signal quality. NB-IoT performance was also found to correspond with theoretical assumptions on coverage and maximum range. However, depending on signal quality, latency was found to vary greatly, resulting in critical degradation of battery life efficiency. Although this study discovered that immense power savings could be achieved by applying critical power management strategies, achieving a long battery life for NB-IoT was not simple. LoRaWAN and Sigfox technologies proved to be more battery efficient than NB-IoT. In a one packet delivery per day scenario, LoRaWAN, Sigfox, and NB-IoT were found to have average battery lives of 1608.9, 1527.6, and 344.9 days, respectively.

Design and optimization of the sustainable natural gas liquefaction process plant: A process system engineering approach

With the rapid development of the world economy and technologies, the energy demand is increasing. Based on the current regulations energy sectors need low-emission and low-carbon energy solutions. Natural gas (NG) is considered a green future fuel that is usually stored and transported as compressed gas or cryogenic liquid. The liquefied NG has 1/600th of its volume which helps in transportation. Liquefied natural gas (LNG) operations are the most energyintensive techniques. During refrigeration and liquefaction, the units of the LNG consume around ∼40−50% of the entire LNG supply chain energy. To date, the simplest single mixed refrigerant (SMR) cycle-based NG liquefaction plants have exergy efficiencies of around 26.97%. Moreover, the conventional SMR process utilizes a hydrocarbon-based mixed refrigerant (i.e., C1 to C3 and N2) mixture that is further harmful to the ecosystem. In this research work, it was found that the addition of eco-friendly hydro-fluoro-olefin (HFO123yf) with conventionally used refrigerants reduces energy consumption under the same conditions. Further hydraulic turbines were proposed to replace the expansion valves require for the Joule Thompson cooling effect that further increasing the process reversibility and energy efficiency. Hence, this research utilizes the optimization of the HFO based modified mixed refrigerant mixture (HSMR) for optimal operation of the LNG plant. The evaluated result shows that the proposed stochastic biogeography-based optimization (BBO) algorithm successfully reduces the overall energy consumption by 0.232 kW/kg-NG. This energy consumption is about 41.709 % compared to our base case study and it was 36% more efficient compared to the conventional SMR published study. This research further explores the sensitive decision variables that affect power consumption using the sensitivity analysis index method. The sensitivity indices may enable us to operate the plant in any abrupt changing or uncertain conditions when needed quick optimization using only a few key sensitive optimizing variables which are useful for real-time plant optimization. Moreover, the economic analysis of the proposed plant shows a total annualized cost of 6,158,000 USD with a 20% rate of return, per year.

Indoor Environmental Quality and Consumption Patterns before and during the COVID-19 Lockdown in Twelve Social Dwellings in Madrid, Spain

This article analyses the situation that prevailed in 12 dwellings located on the outskirts of Madrid during Spain’s state of emergency. How did 24/7 occupation affect the quality of indoor air and power consumption patterns? The mixed method used (surveys and instrumental monitoring) pragmatically detected the variation in consumption, comfort and indoor air quality patterns before and during the COVID-19 pandemic. The characteristics initially in place and household predisposition had a conclusive effect on such variations. The starting conditions, including household composition, habits and the way daily activities were performed, differed widely, logically affecting power consumption: 8/12 case studies increase occupancy density by more than 25 percent; 11/12 improve thermal comfort; 10/12 improve air quality but not necessarily translate in a sufficient ventilation practices; air quality was lower in the bedrooms on the whole; only 4/12 case studies use the potential of passive measures; only one household adopted energy savings strategies; 10/12 case studies increase electric power consumption but none of the dwellings was fitted with a renewable power generation system. The conclusion drawn is that, despite starting conditions differing widely, household composition, habits (including performance of daily activities performance) and power consumption also played an active role in the end result. This approach allowed to integrate qualitative and quantitative findings on indoor environmental quality (IEQ), energy use and households’ behavior. The objective data on the energy situation of the case studies not only is useful for the study, but also for potential enrollment in energy rehabilitation programs, such as the European Regional Development Fund (ERDF).

Past Vector Similarity for Short Term Electrical Load Forecasting at the Individual Household Level

Demand side management (DSM) programs are an integral part of the modern grid. Most of these DSM programs are designed to work at household and hour level. The optimization problems in these DSM programs are guided by the forecasted load. An error in the hour ahead load forecasting results in a suboptimal solution entailing economic cost to both the utility and the customers. Predicting loads at a fine granularity (e.g., households) is challenging due to a large number of (known or unknown) factors affecting power consumption. At larger scales (e.g., clusters of consumers), since the inherent stochasticity and fluctuations are averaged out, the problem becomes substantially easier. Many techniques have been proposed to predict loads of clusters of consumers in various localities with great accuracy. Also, these techniques generally utilize sociological and weather information and work better on data from a particular locality. In this paper, a new technique called Past Vector Similarity (PVS) has been proposed to predict electricity load one hour ahead at the level of an individual household. The proposed strategy is based on load information only and does not require calendar, weather or any other attributes. In fact, the idea is to extract the exact load patterns of individual households corresponding to their routine and socio-economic values. Consequently, the technique makes use of the recent past vector and generate similar patterns for the prediction of future load profiles. Furthermore, the ensemble of these similar loads is an efficient prediction of electricity. PVS has just two parameters due to which it can be applied to the smaller dataset without overfitting issue. Moreover, due to the parallel nature of PVS, it can be scaled for a large number of customers without computation burden. The proposed PVS has been assessed empirically for two distinct datasets from Australia and Sweden. The simulation results demonstrate that the PVS significantly outperforms other state-of-the-art forecasting methods in terms of accuracy.

Комплекс обработки и анализа метеорологических данных в АО "СО ЕЭС" (АС "Метео")

To ensure the operation of Electric Power Systems the System Operator requires significant amount of rapidly updated meteorological data affecting power consumption. The present paper describes Automated system (AS) Meteo software that provides storage and processing of various types of meteorological data, performing analytical calculations, generating reports for daily work and for reporting periods.

Model and Algorithm of Electricity Consumption Management for Household Consumers in the Republic of Tajikistan

The article is devoted to solving the problem of increasing the energy efficiency of urban electrical networks based on identifying factors affecting power consumption. According to the established patterns, a forecasting model has been developed, on the basis of which seasonal schedules of electricity consumption by household consumers are proposed. To monitor and control the operating parameters of the urban electrical network, taking into account the functionality of smart electricity meters, a control algorithm is proposed based on the proposed seasonal power consumption schedules by household consumers and a schematic implementation using smart electricity meters.

Least-Energy Path Planning With Building Accurate Power Consumption Model of Rotary Unmanned Aerial Vehicle

Rotary unmanned aerial vehicles (UAVs), also known as drones, have various advantages, yet their actual applications are limited owing to their flight range. However, increasing the flight range by enhancing the hardware is a challenging task. In this study, we introduce the first step of systematic drone low-power optimization based on the framework of electronic design automation (EDA). We attempt drone power management without in-depth knowledge of aerodynamics and control theory. Instead, we introduce a novel power model of drones using physical parameters that can affect power consumption, such as the three-axis velocity and acceleration, drone height, wind velocity, and the weight and volume of payloads. We detail the experimental setup, power modeling, accuracy verification, and optimization for minimum energy paths. We achieved over 90% accuracy in power modeling without depending on aerodynamics. The proposed approach shows the feasibility of energy-aware rotary UAV flight trajectory optimization considering the external forces affecting drones such as wind. The proposed method presents up to 24.01% energy saving through path changes considering external forces.

Constructing the Industrial Prosperity Index Based on Big Data of Enterprise Electricity

This paper is mainly based on the big electric power data of 5900 industrial enterprises above designated size in Shanghai. By combining a complex network and a hidden Markov model, the prosperity index of 164 medium-sized industries in Shanghai is constructed. Specifically, we use complex networks to describe the correlation between different industries, in order to mine the upstream and downstream drivers that affect industrial power consumption, and on this basis, consider the external factors that affect power consumption to establish a hidden Markov model that predicts changes in power consumption in the industry. Further, we use the state probability output by the Hidden Markov Model to define the industrial prosperity index, hoping that the index can fully reflect the economic operation of various industries in Shanghai and become a “barometer” and “wind vane” for economic development.

Energy consumption and resistive economy: Case Study-Electricity consumption in MENA Countries

Clause 7 of the general policy of resistive economy is explicitly referred to consumption management and consumption pattern reform. One of the important sources of managing consumption is energy. In fact, the need for energy in the world is constantly increasing, while its sources are scarce, so one of the basic steps to achieve economic growth and the realization of a resistive economy is to coordinate this incremental need and scarce resource to optimize its consumption. The factors affecting its consumption should be investigated whether in the field of production as the input of production or in the consumption field as the final product. In this study, the factors affecting power consumption among 16 selected MENA countries through years 2000 to 2015 were investigated using statistical analysis methods and panel data. The results of the model estimation show that the number of mobile subscribers does not have a significant effect on the amount of electricity consumption. Nevertheless, the economic growth variable has a positive and significant effect on the amount of electricity consumption. In addition, the number of Internet users has a significant effect on the electricity consumption.