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Journal articles on the topic 'CRITICAL MACHINE ENERGY'

Author: Grafiati

Published: 11 September 2023

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1

Chen, Chi, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, and Shyue Ping Ong. "A Critical Review of Machine Learning of Energy Materials." Advanced Energy Materials 10, no. 8 (2020): 1903242. http://dx.doi.org/10.1002/aenm.201903242.

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Ohtani, Hisashi. "Development of Energy-Saving Machine Tool." International Journal of Automation Technology 11, no. 4 (2017): 608–14. http://dx.doi.org/10.20965/ijat.2017.p0608.

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Environmental measures are urgently required to realize a society with a low environmental load. In response, various undertakings are being carried out in the field of machine tools, which is most critical in terms of requirement of energy-saving measures. The energy consumed when a machine tool is used to machine can be broadly divided into three categories: the “standby energy” to maintain the electrical devices operational when the machine is not operational; the “steady-state energy,” which is the fixed amount of energy required when the machine is in operation; and the “dynamic energy,”

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Fujishima, Makoto, Hiroshi Shimanoe, and Masahiko Mori. "Reducing the Energy Consumption of Machine Tools." International Journal of Automation Technology 11, no. 4 (2017): 601–7. http://dx.doi.org/10.20965/ijat.2017.p0601.

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Global warming is one of the most important environmental issues that the world faces today. Reducing energy consumption is critical in industrial environments. Machine tools have some of the highest energy consumption rates of all the equipment in factories. This makes it important to reduce machine tool energy consumption to protect the global environment. Some effective ways of reducing the energy consumption of machine tools are by reducing the required energy, shutting down the power to standby mode, and shortening cycle times. This paper introduces several approaches to the reduction of

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Yuan and Sun. "Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing." Sensors 19, no. 12 (2019): 2724. http://dx.doi.org/10.3390/s19122724.

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High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine m

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Alghamdi, Noof Awad, Israa Mohammed Budayr, Samar Mohammed Aljehani, and Majed Mohammed Aborokbah. "A Scheme for Predicting Energy Consumption in Smart Cities Using Machine Learning." Webology 19, no. 1 (2022): 3481–99. http://dx.doi.org/10.14704/web/v19i1/web19230.

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Fluctuating result on weather condition throughout several decades became a global concern due to the direct or indirect effect on energy consumption, and that was well-defined in several sector. Research investigates the use of technology and the speed of obtaining information ، which helps in decision-making. This paper Emphasize the role of data science and their application to monitoring energy consumption, also, explain the importance used and challenges of Internet of Things (IoT). Thus, there is a global concern on data transformation from IoT devices when taking into account deferent w

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Kandil, Abdelrahman, Samir Khaled, and Taher Elfakharany. "Prediction of the equivalent circulation density using machine learning algorithms based on real-time data." AIMS Energy 11, no. 3 (2023): 425–53. http://dx.doi.org/10.3934/energy.2023023.

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<abstract> <p>Equivalent circulation density (ECD) is one of the most important parameters that should be considered while designing drilling programs. With increasing the wells' deep, offshore hydrocarbon extraction, the costly daily rate of downhole measurements, operating restrictions, and the fluctuations in the global market prices, it is necessary to reduce the non-productive time and costs associated with hole problems resulting from ignoring and incorrect evaluation of ECD. Therefore, optimizing ECD and selecting the best drilling parameters are curial tasks in such operati

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RASTGOUFARD, P., and R. A. SCHLUETER. "APPLICATION OF CRITICAL MACHINE ENERGY FUNCTION IN POWER SYSTEM TRANSIENT STABILITY ANALYSIS." Electric Machines & Power Systems 16, no. 5 (1989): 343–61. http://dx.doi.org/10.1080/07313568908909392.

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Vijayapakavan, P., D. S. Robinson Smart, Kurinjimalar Ramu, and M. Ramachandran. "Superconducting Electromagnetic Launch Machine System for Aerospace Applications." Journal on Applied and Chemical Physics 2, no. 1 (2023): 40–47. http://dx.doi.org/10.46632/jacp/2/1/5.

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The aerospace industry is constantly experimenting with innovative technologies to improve efficiency, effectiveness and sustainability. The use of superconducting machines emerged as a promising solution to address the growing demands of Aerospace applications. Superconducting machines offer significant advantages such as higher power density, reduced weight and improved efficiency compared to conventional electrical machines. However, efficient cooling methods are critical to maintain superconducting materials at low-temperature operating conditions. This abstract provides a comprehensive ov

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Cristina Castejon, Cristina, Marıa Jesus Gomez, Juan Carlos Garcia-Prada, and Eduardo Corral. "Energy Distribution Analysis Regarding the Crack Size in a Rotating Shaft." Volume 24, No 3, September 2019 24, no. 3 (2019): 418–25. http://dx.doi.org/10.20855/ijav.2019.24.31190.

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Maintenance is critical to avoid catastrophic failures in rotating machinery, and the detection of cracks plays a critical role because they can originate failures with costly processes of reparation, especially in shafts. Vibration signals are widely used in machine monitoring and fault diagnostics. The most critical issue in machine monitoring is the suitable selection of the vibration parameters that represent the condition of the machine. Discrete Wavelet Transform, and one of its recursive forms, called Wavelet Packet Transform, provide a high potential for pattern extraction. Several fac

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Trontl, Krešimir, Dubravko Pevec, and Tomislav Šmuc. "Machine Learning of the Reactor Core Loading Pattern Critical Parameters." Science and Technology of Nuclear Installations 2008 (2008): 1–6. http://dx.doi.org/10.1155/2008/695153.

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The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear model

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Albdery, Mohsin Hassan, and István Szabó. "A Recent Machine Learning Techniques for Failure Diagnosis of Rolling Element Bearing." Hungarian Agricultural Engineering, no. 39 (2021): 42–53. http://dx.doi.org/10.17676/hae.2021.39.42.

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Rolling element bearings are critical components of rotating machines, and fault in the bearing can cause the machine to fail. Bearing failure is one of the leading causes of failure in various rotating machines used in industry at high and low speeds. Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is an essential element in the diagnosis of defects that saves time and expenses and avoids dangerous conditions. Investigations are being carried

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Neugebauer, Reimund, Carsten Hochmuth, Gerhard Schmidt, and Martin Dix. "Energy Efficient Process Planning Based on Numerical Simulations." Advanced Materials Research 223 (April 2011): 212–21. http://dx.doi.org/10.4028/www.scientific.net/amr.223.212.

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The main goal of energy-efficient manufacturing is to generate products with maximum value-added at minimum energy consumption. To this end, in metal cutting processes, it is necessary to reduce the specific cutting energy while, at the same time, precision requirements have to be ensured. Precision is critical in metal cutting processes because they often constitute the final stages of metalworking chains. This paper presents a method for the planning of energy-efficient machining processes based on numerical simulations. It encompasses two levels of planning flexibility: process adjustment a

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Vyas, Nisarg, Jonathan Farringdon, David Andre, and John Ivo Stivoric. "Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure." AI Magazine 33, no. 2 (2012): 55. http://dx.doi.org/10.1609/aimag.v33i2.2408.

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In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solut

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Rumaherang, Wulfilla M., J. Louhenapessy, Mesak F. Noya, and Cendy S. Tupamahu. "STUDI EKSPERIMENTAL PERFORMANCE KAVITASI WATERJET PROPULSI." ALE Proceeding 4 (August 17, 2021): 112–20. http://dx.doi.org/10.30598/ale.4.2021.112-120.

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Cavitation is a complex phenomenon of dynamic processes in hydraulic machines that can cause a decrease in energy performance, vibration and damage the blade surfaces. Analysis of cavitation symptoms in hydraulic machines is carried out through cavitation performance studies, namely the relations between energy parameters. Each hydraulic machine has a critical value on a different cavitation performance curve. Therefore, a study of the effect of cavitation changes is needed to determine the working zone of hydraulic machines without cavitation. In this study, cavitation performance analysis wa

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Díaz, Pedro-J., Jenny-M. Carvajal, and Miguel-Fernando Palencia-Muñoz. "Double torsion testing machine to determine the subcritical fracture index in rocks." CT&F - Ciencia, Tecnología y Futuro 4, no. 3 (2011): 37–46. http://dx.doi.org/10.29047/01225383.237.

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This paper discusses the design methodology applied to build a testing machine to determine the sub-critical fracture index in a rock, based on double torsion testing in order to characterize naturally fractured formations such as those located in the Colombian Llanos Foothill Basin . These formations have been subjected to cyclic loads over time, causing fractures that trend to spread at sub-critical stress intensity values. Similarly, it presents the results of testing conducted on nine specimens of the Tambor Formation from 2 different outcrops to establish the testing traceability in the e

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Vyas, Nisarg, Jonathan Farringdon, David Andre, and John Stivoric. "Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 2 (2011): 1613–20. http://dx.doi.org/10.1609/aaai.v25i2.18848.

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   In this paper we provide insight into the BodyMedia FIT® armband system— a wearable multi-sensor technology that achieves the goals of continuous physiological monitoring (especially energy expenditure estimation) and weight management using machine learning and data modeling methods. This system has been commercially available since 2001 and more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning tec

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Graffeo, Federica, Silvio Vaschetto, Alessio Miotto, Fabio Carbone, Alberto Tenconi, and Andrea Cavagnino. "Lumped-Parameters Thermal Network of PM Synchronous Machines for Automotive Brake-by-Wire Systems." Energies 14, no. 18 (2021): 5652. http://dx.doi.org/10.3390/en14185652.

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Thermal analysis represents a key factor in electrical machine design due to the impact of temperature increase on insulation lifetime. In this context, there has been a wide investigation on thermal modeling, particularly for machines used in harsh working conditions. In this perspective, brake-by-wire (BBW) systems represent one of the most challenging applications for electrical machines used for automotive smart actuators. Indeed, electro-actuated braking systems are required to repeatedly operate the electric machine in high overload conditions in order to limit the actuator response time

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Puspita Sari, Talitha, Rafin Aqsa Izza Mahendra, Ardyono Priyadi, Vita Lystianingrum, Margo Pujiantara, and Sjamsjul Anam. "Perbaikan CCT Pada Multi Machine Infinite Bus Dengan Supercapacitor Energy Storage Menggunakan Critical Trajectory." Jurnal FORTECH 1, no. 2 (2020): 61–67. http://dx.doi.org/10.32492/fortech.v1i2.225.

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Transient stability is an important aspect in maintaining the continuity and reliability of the electrical power system when a sudden large disturbance occurs. However, there is a time operation's limitation of the protection system to eliminate disturbance before the system becomes unstable and loses its synchronization. Critical Clearing Time (CCT) is the toleration time to eliminate the fault to keep the system stable. To improve system stability, installing Supercapacitor Energy Storage (SCES) can be one of the methods for extending the CCT values. SCES absorb large amounts of electricity

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STEIF, ALAN R. "MULTIPARTICLE SOLUTIONS IN 2+1 GRAVITY AND TIME MACHINES." International Journal of Modern Physics D 03, no. 01 (1994): 277–80. http://dx.doi.org/10.1142/s0218271894000459.

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Multiparticle solutions for sources moving at the speed of light and corresponding to superpositions of single-particle plane-wave solutions are constructed in 2+1 gravity. It is shown that the two-particle spacetimes admit closed timelike curves provided the center-of-momentum energy exceeds a certain critical value. This occurs, however, at the cost of unphysical boundary conditions which are analogous to those affecting Gott’s time machine. As the energy exceeds the critical value, the closed timelike curves first occur at spatial infinity, then migrate inward as the energy is further incre

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Park, Hae Min, Jong Hyuk Lee, and Kyung Doo Kim. "Wall temperature prediction at critical heat flux using a machine learning model." Annals of Nuclear Energy 141 (June 2020): 107334. http://dx.doi.org/10.1016/j.anucene.2020.107334.

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Asif, Muhammad, Hang Shen, Chunlin Zhou, et al. "Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions." Sustainability 15, no. 10 (2023): 8298. http://dx.doi.org/10.3390/su15108298.

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Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings,

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White, G., S. Gessner, E. Adli, et al. "Beam delivery and final focus systems for multi-TeV advanced linear colliders." Journal of Instrumentation 17, no. 05 (2022): P05042. http://dx.doi.org/10.1088/1748-0221/17/05/p05042.

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Abstract The Beam Delivery System (BDS) is a critical component of a high-energy linear collider. It transports the beam from the accelerator and brings it to a focus at the Interaction Point. The BDS system includes diagnostic sections for measuring the beam energy, emittance, and polarization, as well as collimators for machine protection. The length of the BDS increases with collision energy. Higher collision energies also require higher luminosities, and this is a significant constraint on the design for energy-frontier machines. Here, we review BDS designs based on traditional quadrupole

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Rawat, Nishant. "Water Quality Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4173–87. http://dx.doi.org/10.22214/ijraset.2022.44658.

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Abstract: Freshwater is a critical resource for agriculture and industry's survival. Examination of water quality is a fundamental stage in the administration of freshwater assets. As indicated by the World Health Organization's yearly report, many individuals are getting sick or some are dead due to the lack of safe drinking water, especially pregnant ladies and kids. It is critical to test the quality of water prior to involving it for any reason, whether it is for animal watering, chemical spraying(Pesticides etc), or drinking water. Water quality testing is a strategy for finding clean dri

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Syukri, Mahdi. "Analysis of using capacitors in 3-phase induction generators to get constant voltage." Jurnal Geuthèë: Penelitian Multidisiplin 6, no. 1 (2023): 1. http://dx.doi.org/10.52626/jg.v6i1.208.

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Induction machine is one of the machines that can be applied as an alternative power plant and also as a new renewable energy system. Giving reactive power at the terminal in the form of a capacitor, the induction machine can be used as an induction generator. Reactive power is also needed as a voltage generation process. Because one of the weaknesses of the induction generator is that the voltage generated is very fluctuating when the load being served changes. To overcome this, several capacitors with different values are used. Identify the value of the voltage generated by the induction gen

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Braun, S., P. Schraml, and E. Prof Abele. "Energieverbrauchssimulation von Werkzeugmaschinen*/Process-specific energy simulation of machine tools." wt Werkstattstechnik online 106, no. 03 (2016): 163–68. http://dx.doi.org/10.37544/1436-4980-2016-03-67.

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Energie- und Ressourceneffizienz beschreiben Qualitätsmerkmale, die auch für moderne Werkzeugmaschinen gelten. Der Energieverbrauch von Maschinen bis zu gesamten Fertigungsstandorten muss im Verhältnis zur erzielten Wertschöpfung deutlich gesenkt werden, um wettbewerbsfähig zu bleiben und unserer Verantwortung gegenüber der Umwelt zu entsprechen. Der Fachbeitrag präsentiert anhand eines Fräsprozesses ein modellgestütztes Simulations- und Prognosesystem des Energieverbrauchs von kompletten Bearbeitungsoperationen auf einer Werkzeugmaschine als Basis energetischer Optimierungen. Teil 1 des Facha

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Meng, Fanlin, Kui Weng, Balsam Shallal, Xiangping Chen, and Monjur Mourshed. "Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response." Proceedings 2, no. 15 (2018): 1133. http://dx.doi.org/10.3390/proceedings2151133.

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In this paper, we look at the key forecasting algorithms and optimization strategies for the building energy management and demand response management. By conducting a combined and critical review of forecast learning algorithms and optimization models/algorithms, current research gaps and future research directions and potential technical routes are identified. To be more specific, ensemble/hybrid machine learning algorithms and deep machine learning algorithms are promising in solving challenging energy forecasting problems while large-scale and distributed optimization algorithms are the fu

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Marcus, Aaron, and Jérémie Jean. "Going green at home: The Green Machine." Information Design Journal 17, no. 3 (2009): 235–43. http://dx.doi.org/10.1075/idj.17.3.08mar.

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A global challenge for the 21st century is to find a sustainable way of life. The Green movement has helped to increase people’s awareness of sustainability issues and propelled development of innovative products to help decrease our ecological footprint. Smart Grid applications, which enable users to monitor their household’s energy consumption, are one of these innovative products. Critical data visualization helps to build awareness, but does not result automatically in effecting behavioral changes, which are required to ensure the Earth’s future and survival. The question then shifts to ho

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Kravets, Svyatoslav, Vladimir Suponyev, and Aleksej Goponov. "Determination of critical depth forces of cutting soils and energy consumption of chain scraper trench excavators." Bulletin of Kharkov National Automobile and Highway University 1, no. 92 (2021): 192. http://dx.doi.org/10.30977/bul.2219-5548.2021.92.1.192.

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The presented results of scientific research are aimed at solving the problem, which is associated with an increase in the productivity of the development of trenches for laying engineering communications due to the use of new less energy-intensive soil development processes with the working equipment of chain trench excavators. The aim of the work is to establish the regularities of the interaction of the working equipment of the chain scraper excavator with the soil, in which the cutters work in the critical cutting depth mode. Among the tasks that are directed to achieve the goal, it was ne

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Patil, Prof Sachin Sambhaji, Mahesh Manohar Sirsat, Ajitkumar Vishwakarma Sharma, Aashish Shahi, and Omkar Maruti Halgi. "Web Based Machine Learning Automated Pipeline." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1875–78. http://dx.doi.org/10.22214/ijraset.2023.50406.

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Abstract: With the increasing volume, velocity, veracity, and variety of data, it has become critical to have efficient techniques and tools for managing and analyzing data in machine learning. Abstraction is a powerful concept that allows users to interact with machine learning algorithms without understanding their technical implementation details. In this project the user will provide the dataset in .csv format the dataset is then processed further to different machine learning preprocessing steps like removing unwanted columns, handling missing values, label encoding, outlier detection and

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Ding, Yakui, Yongping Li, Heran Zheng, Jing Meng, Jing Lv, and Guohe Huang. "Identifying critical energy-water paths and clusters within the urban agglomeration using machine learning algorithm." Energy 250 (July 2022): 123880. http://dx.doi.org/10.1016/j.energy.2022.123880.

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Razzak, Imran, Guandong Xu, and Muhammad Khurram Khan. "Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures." IEEE Transactions on Industrial Informatics 18, no. 5 (2022): 3449–51. http://dx.doi.org/10.1109/tii.2021.3128962.

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Peplow, Andrew, Javad Isavand, Afshar Kasaei, Babak Afzali, and Delphine Bard-Hagberg. "A Speed-Variant Balancing Method for Flexible Rotary Machines Based on Acoustic Responses." Sustainability 13, no. 13 (2021): 7237. http://dx.doi.org/10.3390/su13137237.

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As rotary machines have become more complicated, balancing processes have been classified as a vital step in condition monitoring to ensure that machines operate reliably, smoothly and safely. All rotating objects will deflect during rotation and all objects possess certain natural frequencies in the absence of rotation. However, an unbalanced object can cause significant unwanted deflection created by resonant vibration at a frequency (cycles/second) close to certain rotational speeds (rotations/second), known as critical speeds. This is especially important for flexible machines which normal

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Jason, Sebagenzi. "Real-time Virtual Machine Energy-Efficient Allocation in Cloud Data Centers Using Interval-packing Methods." Transactions on Machine Learning and Artificial Intelligence 10, no. 6 (2022): 15–34. http://dx.doi.org/10.14738/tmlai.106.13419.

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The reduction of power consumption, which can lower the operation costs of Cloud providers, lengthen the useful life of a machine, as well as lessen the environmental effect caused by power consumption, is one of the critical concerns for large-scale Cloud applications. To satisfy the needs of various clients, Virtual Machines (VMs) as resources (Infrastructure as a Service (IaaS)) can be dynamically allocated in cloud data centers. In this research, we study the energy-efficient scheduling of real-time VMs by taking set processing intervals into account, with the providers' goal of lowering p

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Priyadi, Ardyono, Nazila Iyyaya Fariha, Talitha Puspita Sari, Vita Lystianingrum, Margo Pujiantara, and Sjamsjul Anam. "Efek Penambahan SCES Pada Sistem Multimesin dengan Damping dan Kontroler Berdasarkan Metode Critical Trajectory." Jurnal FORTECH 1, no. 2 (2020): 79–84. http://dx.doi.org/10.32492/fortech.v1i2.228.

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The system can maintain its synchronization under a transient condition. The transient stability system can be evaluated using the critical trajectory method by calculating the Critical Clearing Time (CCT). The advantage of using a critical trajectory method is that the CCT can be found directly and more accurate than the time domain simulation method. This paper proposed the addition of Supercapacitor Energy Storage (SCES) and damping to enhance the transient stability on multi machine system. SCES is an electrical energy storage device that quickly stores and supplies large amounts of electr

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Chen, Chun-Wei, Chun-Chang Li, and Chen-Yu Lin. "Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System." Energies 13, no. 17 (2020): 4368. http://dx.doi.org/10.3390/en13174368.

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Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were u

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Salonitis, Konstantinos. "Energy efficiency assessment of grinding strategy." International Journal of Energy Sector Management 9, no. 1 (2015): 20–37. http://dx.doi.org/10.1108/ijesm-04-2013-0009.

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Purpose – This paper aims to set the framework for measuring the energy performance of a manufacturing process. The availability and affordability of energy is becoming a critical parameter nowadays, affecting the whole lifecycle of the product, and hence the production phase as well. The energy efficiency of the grinding process, as a widely used manufacturing process in the industry, is assessed with regard to the selected process strategies. Design/methodology/approach – To assess the grinding machine tool energy performance, a measuring framework is designed, implemented and validated. The

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Kishore, Somasundaram Chandra, Suguna Perumal, Raji Atchudan, Muthulakshmi Alagan, Ashok K. Sundramoorthy, and Yong Rok Lee. "A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis." Catalysts 12, no. 7 (2022): 743. http://dx.doi.org/10.3390/catal12070743.

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In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deteri

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Naik, Ketaki Bhalchandra, G. Meera Gandhi, and S. H. Patil. "Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre." Cybernetics and Information Technologies 18, no. 3 (2018): 23–36. http://dx.doi.org/10.2478/cait-2018-0036.

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Abstract Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the execution perspective are fulfilled by scaling up and down the Virtual Machines (VMs). The appropriate selection of VMs to handle the unpredictable peak workload without load imbalance is a critical challenge for a cloud data center. In this article, we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II (G-NSGA2) for agile selection of a virtual machine. Our strategy generates Pareto optimal solutions for fair

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Yılmaz, Beyza, and Ramazan Yıldırım. "Critical review of machine learning applications in perovskite solar research." Nano Energy 80 (February 2021): 105546. http://dx.doi.org/10.1016/j.nanoen.2020.105546.

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Agarala, Ajaysekhar, Sunil S. Bhat, Arghya Mitra, Daria Zychma, and Pawel Sowa. "Transient Stability Analysis of a Multi-Machine Power System Integrated with Renewables." Energies 15, no. 13 (2022): 4824. http://dx.doi.org/10.3390/en15134824.

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The impact on the stability of power systems is rising as the penetration level of renewable energy with sporadic natures rises rapidly on the grid. However, the impact of different types of renewable energy sources (wind, solar) and their combination on system stability varies even with the same penetration level. This paper concentrates mainly on the stability analysis of multi-machine systems connected to various types of renewable energy sources. The study presents a simple and novel control technique named automatic reactive power support (ARS) for both single and combinations of renewabl

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Junaid, Muhammad, Adnan Sohail, Fadi Al Turjman, and Rashid Ali. "Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSO." ACM Transactions on Internet Technology 22, no. 1 (2022): 1–35. http://dx.doi.org/10.1145/3433541.

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Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly

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Choi, Gilsu. "Analysis and Experimental Verification of the Demagnetization Vulnerability in Various PM Synchronous Machine Configurations for an EV Application." Energies 14, no. 17 (2021): 5447. http://dx.doi.org/10.3390/en14175447.

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Safety is a critical feature for all passenger vehicles, making fail–safe operation of the traction drive system highly important. Increasing demands for traction drives that can operate in challenging environments over wide constant power speed ranges expose permanent magnet (PM) machines to conditions that can cause irreversible demagnetization of rotor magnets. In this paper, a comprehensive analysis of the demagnetization vulnerability in PM machines for an electric vehicle (EV) application is presented. The first half of the paper presents rotor demagnetization characteristics of several

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Husainy, Avesahemad S. N., Sairam A. Patil, Atharva S. Sinfal, Vasim M. Mujawar, and Chandrashekhar S. Sinfal. "Parameter Optimization of Refrigeration Chiller by Machine Learning." Asian Journal of Electrical Sciences 12, no. 1 (2023): 39–45. http://dx.doi.org/10.51983/ajes-2023.12.1.3684.

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The implementation of machine learning in a chiller system provides several benefits. It can improve energy efficiency by optimizing chiller operation based on predicted load requirements. It can enhance system reliability and reduce maintenance costs by detecting and diagnosing faults in advance. Furthermore, it can enable data-driven decision-making, enabling operators to make informed choices based on accurate predictions and insights. This implementation aims to leverage machine learning techniques to optimize the performance and energy efficiency of a chiller system. Chiller systems are w

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RECHTIN, CYDNEY, CHITTA RANJAN, ANTHONY LEWIS, and BETH ANN ZARKO. "Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning." November 2019 18, no. 11 (2019): 679–89. http://dx.doi.org/10.32964/tj18.11.679.

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Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain

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Scafà, Martina, Marco Marconi, and Michele Germani. "A critical review of symbiosis approaches in the context of Industry 4.0☆." Journal of Computational Design and Engineering 7, no. 3 (2020): 269–78. http://dx.doi.org/10.1093/jcde/qwaa022.

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Abstract The implementation of symbiosis approaches is recognized as an effective industrial strategy towards the optimization of resource exploitation and the improvement of collaboration in the context of Industry 4.0. An industrial system can be considered as a complex environment in which material, energy, machine, and human resources should cooperate towards the improvement of efficiency and the creation of value. According to this vision, the paper presents a detailed literature review about the existing symbiosis approaches: (i) industrial symbiosis models, which mainly aim at the shari

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AlHaddad, Ulaa, Abdullah Basuhail, Maher Khemakhem, Fathy Elbouraey Eassa, and Kamal Jambi. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events." Sustainability 15, no. 16 (2023): 12622. http://dx.doi.org/10.3390/su151612622.

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The critical challenge of enhancing the resilience and sustainability of energy management systems has arisen due to historical outages. A potentially effective strategy for addressing outages in energy grids involves preparing for future failures resulting from line vulnerability or grid disruptions. As a result, many researchers have undertaken investigations to develop machine learning-based methodologies for outage forecasting for smart grids. This research paper proposed applying ensemble methods to forecast the conditions of smart grid devices during extreme weather events to enhance the

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Borunda, Monica, Adrián Ramírez, Raul Garduno, Gerardo Ruíz, Sergio Hernandez, and O. A. Jaramillo. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning." Energies 15, no. 23 (2022): 8895. http://dx.doi.org/10.3390/en15238895.

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Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. This paper proposes a fast-track methodology to address these two critical requirements when exploring a vast area to locate, in a first approximation, potential sites to build PV plants. This methodology retrieves solar radiation and temperature data from free access databases for the ar

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Chen, James Ming, and Mobeen Ur Rehman. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities." Energies 14, no. 19 (2021): 6099. http://dx.doi.org/10.3390/en14196099.

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The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied

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Del Ser, J., D. Casillas-Perez, L. Cornejo-Bueno, et al. "Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives." Applied Soft Computing 118 (March 2022): 108526. http://dx.doi.org/10.1016/j.asoc.2022.108526.

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Forootan, Mohammad Mahdi, Iman Larki, Rahim Zahedi, and Abolfazl Ahmadi. "Machine Learning and Deep Learning in Energy Systems: A Review." Sustainability 14, no. 8 (2022): 4832. http://dx.doi.org/10.3390/su14084832.

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With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy sy

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