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Parallel Statistical and Machine Learning Methods for Estimation of Physical Load [chapter]

Sergii Stirenko, Peng Gang, Wei Zeng, Yuri Gordienko, Oleg Alienin, Oleksandr Rokovyi, Nikita Gordienko, Ivan Pavliuchenko, Anis Rojbi
2018 Smart Environment for Smart Cities  
Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue .  ...  Several metrics for estimation of the instant effect and accumulated effect (physical fatigue) of physical loads were proposed.  ...  University (Huizhou, P.R.China) in the framework of Platform Construction for China-Ukraine Hi-Tech Park Project # 2014C050012001.  ... 
doi:10.1007/978-3-030-05051-1_33 dblp:conf/ica3pp/StirenkoGZGARGP18 fatcat:f6e6zfsanndbnaki6ftg32rfem

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Yuanjun Guo, Zhile Yang, Shengzhong Feng, Jinxing Hu
2018 Complexity  
This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems.  ...  To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China (51607177, 61433012, and U1435215), Shenzhen Science and Technology Innovation Commission application demonstration  ... 
doi:10.1155/2018/8496187 fatcat:2esxs54ixjbx7ijvct7hbffyji

Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks [article]

Anshul Ramachandran, Ashwin Balakrishna, Peter Kundzicz, Anirudh Neti
2018 arXiv   pre-print
We have shown this quickly gives accurate estimates of average usage statistics given a proposed configuration, without the need for running many computationally expensive simulations.  ...  EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new  ...  Description of input preprocessing methods and machine learning architectures can be found in Sections III.E and III.F respectively.  ... 
arXiv:1804.00714v1 fatcat:6h7qjw272jehliq33obwjqjrv4

Parallelization Primitives for Dynamic Sparse Computations

Tsung-Han Lin, Stephen J. Tarsa, H. T. Kung
2013 USENIX Conference on Hot Topics in Parallelism  
We characterize a general class of algorithms common in machine learning, scientific computing, and signal processing, whose computational dependencies are both sparse, and dynamically defined throughout  ...  Existing parallel computing runtimes, like MapReduce and GraphLab, are a poor fit for this class because they assume statically defined dependencies for resource allocation and scheduling decisions.  ...  We seek a program abstraction appropriate for parallelizing dynamic sparse computations in machine learning, scientific computing, and signal processing applica-tions.  ... 
dblp:conf/hotpar/LinTK13 fatcat:i7p4oil3tbggbgkjvhfq6gjr2i

Numerical magnetic field analysis and signal processing for fault diagnostics of electrical machines

S. Pöyhönen, M. Negrea, P. Jover, A. Arkkio, H. Hyötyniemi
2003 Compel  
The stator line current, circulating currents between parallel stator branches and forces between the stator and rotor are compared as media of fault detection.  ...  Numerical magnetic field analysis is used for predicting the performance of an induction motor and a slip-ring generator having different faults implemented in their structure.  ...  SVM for multi-class classification SVM based classification is a relatively new machine learning method based on the statistical learning theory presented by Vapnik (1998) .  ... 
doi:10.1108/03321640310482931 fatcat:33kh67odezhchk7ntyjdkquvpe

Sensorless Current Prediction in Single Phase Inverter Circuits with Machine Learning Algorithms

Hüseyin TÜRE, Selami BALCI, Kadir SABANCI, Muhammet Fatih ASLAN
2021 Journal of Energy Systems  
Through the data obtained, the output current of a phase inverter without a sensor is estimated by Machine Learning Algorithms (MLA) such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) and  ...  The control signal applied to the switching elements can affect the quality of the sinusoidal signal that occurs at the output of the inverter circuit by the means of voltage and current values.  ...  Machine learning algorithms MLP, KNN and SVM are used for current estimation of single-phase inverter.  ... 
doi:10.30521/jes.932581 fatcat:eu3rcifsl5e7bgpr37ii4r5lo4

Optimising Bootstrapping Algorithms Using R and Hadoop

Shicai Wang, Mihaela A. Mares, Yike Guo
2015 2015 IEEE 35th International Conference on Distributed Computing Systems Workshops  
A key research problem in machine learning and statistics today is feature or variable selection when the number of samples is relatively small to the number of features.  ...  In this paper, we introduce an efficient data distribution and load balanced parallel calculation for the Bolasso algorithm based on R and HDFS.  ...  RELATED WORKS R is a widely used analysis tool in statistics and machine learning applications.  ... 
doi:10.1109/icdcsw.2015.34 dblp:conf/icdcsw/WangMG15 fatcat:vw6z3ukhjva6hnbdgqmxgdlieq

A Multiprocessing-based sensitivity analysis of Machine Learning algorithms for Load Forecasting of Electric Power Distribution System

Ameema Zainab, Dabeeruddin Syed, Ali Ghrayeb, Haitham Abu-Rub, Shady S. Refaat, Mahdi Houchati, Othmane Bouhali, Santiago Banales Lopez
2021 IEEE Access  
INDEX TERMS Big data applications, machine learning algorithms, parallel processing, load forecast, smart grids.  ...  The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability.  ...  ACKNOWLEDGMENT The findings achieved herein are solely the responsibility of the author[s]. The Open Access Funding was provided by Qatar National Library.  ... 
doi:10.1109/access.2021.3059730 fatcat:g6ybn5uo5fhrrfmalicfw5m42u

Platform to Build the Knowledge Base by Combining Sensor Data and Context Data

Sungho Shin, Jungho Um, Dongmin Seo, Sung-Pil Choi, Seungwoo Lee, Hanmin Jung, Mun Yong Yi
2014 International Journal of Distributed Sensor Networks  
Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data  ...  Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with  ...  Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.  ... 
doi:10.1155/2014/542764 fatcat:5vjcyycgffc57nlz6cpzgmnknm

ELECTRICITY BILL PRICE FORECASTING WITH ARIMA MODEL USING THE CONCEPT OF MACHINE LEARNING

Anjali Krishnan
2019 International Journal of Information Systems and Computer Sciences  
For feature selection kernel principal component analysis is being used.  ...  Price forecasting has always played a pivotal role in the success of every institution or life course. Electricity price forecasting is one of the key elements among price forecasting.  ...  Therefore, for now, CSS and mle refer to estimation method only.  ... 
doi:10.30534/ijiscs/2019/33822019 fatcat:osu2ajygw5cb3kveihj6a5l6pi

Surrogate modelling of VLE: Integrating machine learning with thermodynamic constraints

Andres Carranza-Abaid, Hallvard F. Svendsen, Jana P. Jakobsen
2020 Chemical Engineering Science: X  
An easy-to-implement methodology to develop accurate, fast and thermodynamically consistent surrogate machine learning (ML) models for multicomponent phase equilibria is proposed.  ...  The accuracy of the surrogate model predictions of VLE for this system is found to be satisfactory as the results provide an average absolute relative difference of 0.50% compared to the estimates obtained  ...  Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper  ... 
doi:10.1016/j.cesx.2020.100080 fatcat:7wcvqdax2bcv3d2fvulp5uxdgu

Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency

Hooman Farzaneh, Ladan Malehmirchegini, Adrian Bejan, Taofeek Afolabi, Alphonce Mulumba, Precious P. Daka
2021 Applied Sciences  
In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing  ...  The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency  ...  Acknowledgments: The authors wish to thank the editor and the reviewers for their contributions on the paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11020763 fatcat:3ipak4rmyba67jdrds6fpyuple

Big Data-Driven Detection of False Data Injection Attacks in Smart Meters

Fatih Unal, Abdulaziz Almalaq, Sami Ekici, Patrick Glauner
2021 IEEE Access  
state-of-the-art machine learning algorithms used for NTL classification problems.  ...  In this paper, we propose a novel Big Data-driven solution that employs machine learning, deep learning and parallel computing techniques.  ...  ACKNOWLEDGMENT The authors would like to thank Mahmut Sami Sarac and Tolga Koc (Department of Automatic Meter Reading (AMR) System, Dicle Elektrik Perakende Satış A.Ş.) for providing helpful insights and  ... 
doi:10.1109/access.2021.3122009 fatcat:amu73zeutfflxnragfzxexkutq

Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis

Usman Ali, Mohammad Haris Shamsi, Cathal Hoare, Eleni Mangina, James O'Donnell
2021 Energy and Buildings  
This paper proposes Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of approaches, methods and tools used for urban building energy modeling.  ...  These studies lack an in-depth discussion of the challenges and future research opportunities related to data-driven, reduced-order, and simulation-based modeling methods.  ...  The opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the SFI.  ... 
doi:10.1016/j.enbuild.2021.111073 fatcat:rsr7gemb3jhozaa3l7wktrqk3q

Guest Editorial: Industrial Cyber–Physical Systems—New Trends in Computing and Communications

Federico Tramarin, Michele Luvisotto, Andreas Willig, Kan Yu
2021 IEEE Transactions on Industrial Informatics  
Machine Learning, Estimation, and Control The last years have witnessed a revolution in the field of machine learning.  ...  Another example of how machine learning can improve communication in ICPS is given in "Learning-based Online Transmission Path Selection for Secure Estimation in Edge Computing Systems," authored by Wang  ...  Luvisotto has served as a Guest Editor for IEEE Access and the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, and he contributed to the organization of several IEEE international conferences.  ... 
doi:10.1109/tii.2020.3033818 fatcat:d6mi5ycnpnhttkvmfbe75ejrdy
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