Residential Electrical Load Model Based on Mixture Model Clustering and Markov Models

被引:110
作者
Labeeuw, Wouter [1 ]
Deconinck, Geert [1 ]
机构
[1] Katholieke Univ Leuven, B-3001 Louvain, Belgium
关键词
Clustering; data analysis; Markov models; statistical distributions; DEMAND RESPONSE; CLASSIFICATION; CUSTOMER; IDENTIFICATION; BUILDINGS; OPERATION; PROFILE;
D O I
10.1109/TII.2013.2240309
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
Detailed large-scale simulations require a lot of data. Residential electrical load profiles are well protected by privacy laws. Representative residential electrical load generators get around the privacy problem and allow for Monte Carlo simulations. A top-down model of the residential electrical load, based on a dataset of over 1300 load profiles, is presented in this paper. The load profiles are clustered by a Mixed Model to group similar ones. Within the group, a behavior model is constructed with a Markov model. The states of the Markov models are based on the probability distribution of the electrical power. A second Markov model is created to randomize the behavior. A load profile is created by first performing a random-walking of the Markov models to get a sequence of states. The inverse of the probability distribution of the electrical power is used to translate the resulting states into electrical power.
引用
收藏
页码:1561 / 1569
页数:9
相关论文
共 38 条
[1]
[Anonymous], THESIS HELSINKI U TE
[2]
[Anonymous], 1961, Adaptive Control Processes: a Guided Tour, DOI DOI 10.1515/9781400874668
[3]
Behavior Learning in Dwelling Environments With Hidden Markov Models [J].
Bruckner, Dietmar ;
Velik, Rosemarie .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2010, 57 (11) :3653-3660
[4]
A BOTTOM-UP APPROACH TO RESIDENTIAL LOAD MODELING [J].
CAPASSO, A ;
GRATTIERI, W ;
LAMEDICA, R ;
PRUDENZI, A .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1994, 9 (02) :957-964
[5]
Smart Operation of Wind Turbines and Diesel Generators According to Economic Criteria [J].
Cecati, Carlo ;
Citro, Costantino ;
Piccolo, Antonio ;
Siano, Pierluigi .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2011, 58 (10) :4514-4525
[6]
Comparisons among clustering techniques for electricity customer classification [J].
Chicco, G ;
Napoli, R ;
Piglione, F .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) :933-940
[7]
Load pattern-based classification of electricity customers [J].
Chicco, G ;
Napoli, R ;
Piglione, F ;
Postolache, P ;
Scutariu, M ;
Toader, C .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2004, 19 (02) :1232-1239
[8]
The impact of vehicle-to-grid on the distribution grid [J].
Clement-Nyns, Kristien ;
Haesen, Edwin ;
Driesen, Johan .
ELECTRIC POWER SYSTEMS RESEARCH, 2011, 81 (01) :185-192
[9]
Random effects mixture models for clustering electrical load series [J].
Coke, Geoffrey ;
Tsao, Min .
JOURNAL OF TIME SERIES ANALYSIS, 2010, 31 (06) :451-464
[10]
Optimal Generation Mix With Short-Term Demand Response and Wind Penetration [J].
De Jonghe, Cedric ;
Hobbs, Benjamin F. ;
Belmans, Ronnie .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2012, 27 (02) :830-839