Fuzzy C-Means Clustering Applied to Load Profiling of Industrial Customers

被引:5
作者
Dedic, Adisa [1 ]
Konjic, Tatjana [2 ]
Calasan, Martin [3 ]
Dedic, Zehrudin [4 ]
机构
[1] JP EP BiH, Publ Enterprise Elect Power Ind Bosnia & Herzegov, Tuzla, Bosnia & Herceg
[2] Univ Tuzla, Fac Elect Engn, Tuzla, Bosnia & Herceg
[3] Univ Montenegro, Fac Elect Engn, Podgorica 81000, Montenegro
[4] Deling Doo, Tuzla, Bosnia & Herceg
关键词
fuzzy c-means clustering; load profiling; industrial customer classification; load shape factor; statistical analysis; load diagram analysis; DAILY ELECTRICAL LOAD; CLASSIFICATION; TIME;
D O I
10.1080/15325008.2022.2049660
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Nowadays, knowing load profiles of different customers in electricity market-oriented operation of power system is very important. Smart metering system provides more than enough information about it. However, even if there is a load measurement on each customer in the system, it is economically unjustified and it is time consuming to conduct a detailed analysis each of them. In this study, unlike literature approaches, the different industrial load profiles are observed. The fuzzy c-means clustering is applied to get typical load curves representing different type of industry. For that purpose, the detailed statistical analysis and calculations of load shape factors of the industrial customers for different period of the year have been carried out. Customer's 15-min load was collected by developing Advanced Metering Infrastructure in the power system of Bosnia and Herzegovina. According to available data, statistical analysis and applied fuzzy c-means clustering, three typical load profiles for different industrial customers are proposed.
引用
收藏
页码:1068 / 1084
页数:17
相关论文
共 42 条
  • [1] Electrical Customer Profile Using Fuzzy Logic Theory
    Abreu, T.
    Minussi, C.
    Lopes, M.
    Alves, U.
    Lotufo, A.
    [J]. IEEE LATIN AMERICA TRANSACTIONS, 2020, 18 (08) : 1353 - 1361
  • [2] A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid
    Aly, Hamed H. H.
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2020, 182
  • [3] Smart Grid: Overview, Issues and Opportunities. Advances and Challenges in Sensing, Modeling, Simulation, Optimization and Control
    Amin, S. Massoud
    [J]. EUROPEAN JOURNAL OF CONTROL, 2011, 17 (5-6) : 547 - 567
  • [4] Clustering of electrical load patterns and time periods using uncertainty-based multi-level amplitude thresholding
    Charwand, Mansour
    Gitizadeh, Mohsen
    Siano, Pierluigi
    Chicco, Gianfranco
    Moshavash, Zeinab
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2020, 117
  • [5] Comparisons among clustering techniques for electricity customer classification
    Chicco, G
    Napoli, R
    Piglione, F
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) : 933 - 940
  • [6] Customer characterization options for improving the tariff offer
    Chicco, G
    Napoli, R
    Postolache, P
    Scutariu, M
    Toader, CM
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2003, 18 (01) : 381 - 387
  • [7] Renyi entropy-based classification of daily electrical load patterns
    Chicco, G.
    Akilimali, J. Sumaili
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2010, 4 (06) : 736 - 745
  • [8] Chicco G., 2021, Local Electricity Markets, P215
  • [9] Support Vector Clustering of Electrical Load Pattern Data
    Chicco, Gianfranco
    Ilie, Irinel-Sorin
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (03) : 1619 - 1628
  • [10] Feature based clustering technique for investigation of domestic load profiles and probabilistic variation assessment: Smart meter dataset
    Choksi, Kushan Ajay
    Jain, Sonal
    Pindoriya, Naran M.
    [J]. SUSTAINABLE ENERGY GRIDS & NETWORKS, 2020, 22