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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clustering Techniques in Load Profile Analysis for Distribution Stations

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Download PDF pdficon (501 KB) | Citation | Downloads: 1,071 | Views: 6,932

Author keywords
load profile, clustering techniques, data flow analysis, power consumption, distribution station

References keywords
power(5), load(5), clustering(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2009-02-03
Volume 9, Issue 1, Year 2009, On page(s): 63 - 66
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.01011
Web of Science Accession Number: 000264815300011
SCOPUS ID: 67749137135

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The demand characteristic is the most important one in analyzing customer information. In a distribution network, there is in any moment certain degree of uncertainty about busses loads, and consequently, about load level of network, busses voltage level, and power losses. Therefore, it is very important to estimate first of all the load profiles of buses, using available data (measurements effectuated in distribution stations). The results obtained for various distribution stations demonstrate the effectiveness of the present method in overcoming the difficulties encountered in optimal planning and operation of distribution networks.

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SCOPUS® Times Cited: 28
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Cited-By CrossRef

[1] Optimal Clustering of Time Periods for Electricity Demand-Side Management, Rogers, David F., Polak, George G., IEEE Transactions on Power Systems, ISSN 0885-8950, Issue 4, Volume 28, 2013.
Digital Object Identifier: 10.1109/TPWRS.2013.2252373

[2] Machine learning based analysis of factory energy load curves with focus on transition times for anomaly detection, Flick, Dominik, Keck, Claudio, Herrmann, Christoph, Thiede, Sebastian, Procedia CIRP, ISSN 2212-8271, Issue , 2020.
Digital Object Identifier: 10.1016/j.procir.2020.04.073

[3] The Impact of the Load Side Parameters on PC Cluster's Harmonics Emission, KATIC, V. A., MUJOVIC, S. V., RADULOVIC, V. M., RADOVIC, J. S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 11, 2011.
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[4] Two-Stage Load Pattern Clustering Using Fast Wavelet Transformation, Mets, Kevin, Depuydt, Frederick, Develder, Chris, IEEE Transactions on Smart Grid, ISSN 1949-3053, Issue 5, Volume 7, 2016.
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[5] Robust Real-Time Load Profile Encoding and Classification Framework for Efficient Power Systems Operation, Varga, Ervin D., Beretka, Sandor F., Noce, Christian, Sapienza, Gianluca, IEEE Transactions on Power Systems, ISSN 0885-8950, Issue 4, Volume 30, 2015.
Digital Object Identifier: 10.1109/TPWRS.2014.2354552

[6] A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures, Moussa, Sherin, Mastorakis, N., Mladenov, V., Bulucea, A., MATEC Web of Conferences, ISSN 2261-236X, Issue , 2016.
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[7] Classification of daily electric load profiles of non-residential buildings, Bourdeau, Mathieu, Basset, Philippe, Beauchêne, Solène, Da Silva, David, Guiot, Thierry, Werner, David, Nefzaoui, Elyes, Energy and Buildings, ISSN 0378-7788, Issue , 2021.
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[8] Statistical models for disaggregation and reaggregation of natural gas consumption data, Brabec, M., Konár, O., Malý, M., Kasanický, I., Pelikán, E., Journal of Applied Statistics, ISSN 0266-4763, Issue 5, Volume 42, 2015.
Digital Object Identifier: 10.1080/02664763.2014.993365

[9] Load profile analysis for reducing energy demands of production systems in non-production times, Dehning, Patrick, Blume, Stefan, Dér, Antal, Flick, Dominik, Herrmann, Christoph, Thiede, Sebastian, Applied Energy, ISSN 0306-2619, Issue , 2019.
Digital Object Identifier: 10.1016/j.apenergy.2019.01.047

[10] Least Squares Modeling of Voltage Harmonic Distortion Due to PC Cluster Operation, MUJOVIC, S., DJUKANOVIC, S., RADULOVIC, V., KATIC, V., RASOVIC, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 13, 2013.
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[11] Strategies for Power/Energy Saving in Distribution Networks, GRIGORAS, G., CARTINA, G., BOBRIC, E. C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 10, 2010.
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[12] Voltage and reactive power control in a distribution system using hybrid SA-DBPSO algorithm, Rahideh, Abdolhamid, Gitizadeh, Mohsen, Sadrzadeh, Ali, 2015 20th Conference on Electrical Power Distribution Networks Conference (EPDC), ISBN 978-1-4673-6612-0, 2015.
Digital Object Identifier: 10.1109/EPDC.2015.7330476

[13] Using Significant Classification Rules to Analyze Korean Customers' Power Consumption Behavior: Incremental Tree Induction using Cascading-and-Sharing Method, Piao, Minghao, Li, Meijing, Ryu, Keun Ho, 2010 10th IEEE International Conference on Computer and Information Technology, ISBN 978-1-4244-7547-6, 2010.
Digital Object Identifier: 10.1109/CIT.2010.503

[14] Data-driven residential customer aggregation based on seasonal behavioral patterns, Chen, Kunjin, Hu, Jun, He, Ziyu, 2017 IEEE Power & Energy Society General Meeting, ISBN 978-1-5386-2212-4, 2017.
Digital Object Identifier: 10.1109/PESGM.2017.8273738

[15] Efficient encoding of customer class load profiles, Beretka, Sandor F., Varga, Ervin D., 2013 Africon, ISBN 978-1-4673-5943-6, 2013.
Digital Object Identifier: 10.1109/AFRCON.2013.6757767

[16] Optimal BESS Scheduling Strategy in Microgrids Based on Genetic Algorithms, Sidea, Dorian-Octavian, Toma, Lucian, Sanduleac, Mihai, Picioroaga, Irina-Ioana, Boicea, Valentin-Adrian, 2019 IEEE Milan PowerTech, ISBN 978-1-5386-4722-6, 2019.
Digital Object Identifier: 10.1109/PTC.2019.8810633

[17] Locality sensitive hashing of customer load profiles, Beretka, Sandor F., Varga, Ervin D., 2013 International Conference on Renewable Energy Research and Applications (ICRERA), ISBN 978-1-4799-1464-7, 2013.
Digital Object Identifier: 10.1109/ICRERA.2013.6749779

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