Modeling and Validation of Electrical Load Profiling in Residential Buildings in Singapore

被引:103
作者
Chuan, Luo [1 ]
Ukil, Abhisek [1 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
关键词
AMI; building energy; demand side management; DSM; energy efficiency; energy portal; load modeling; load profile; low voltage; LV; smart grid; smart meter; CONSUMPTION; NETWORKS;
D O I
10.1109/TPWRS.2014.2367509
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
The demand of electricity keeps increasing in this modern society and the behavior of customers vary greatly from time to time, city to city, type to type, etc. Generally, buildings are classified into residential, commercial and industrial. This study is aimed to distinguish the types of residential buildings in Singapore and establish a mathematical model to represent and model the load profile of each type. Modeling household energy consumption is the first step in exploring the possible demand response and load reduction opportunities under the smart grid initiative. Residential electricity load profiling includes the details on the electrical appliances, its energy requirement, and consumption pattern. The model is generated with a bottom-up load model. Simulation is performed for daily load profiles of 1 or 2 rooms, 3 rooms, 4 rooms and 5 rooms public housing. The simulated load profile is successfully validated against the measured electricity consumption data, using a web-based Customer Energy Portal (CEP) at the campus housings of Nanyang Technological University, Singapore.
引用
收藏
页码:2800 / 2809
页数:10
相关论文
共 27 条
[1]  
Ajay P., 2009, J ENG SCI TECH REV, V2, P141
[2]   Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system [J].
Bennett, Christopher J. ;
Stewart, Rodney A. ;
Lu, Jun Wei .
ENERGY, 2014, 67 :200-212
[3]   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
[4]  
Cheah P. H., 2012, P INT C POW EN IPEC
[5]   Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction [J].
Deihimi, Ali ;
Orang, Omid ;
Showkati, Hemen .
ENERGY, 2013, 57 :382-401
[6]  
EMA, 2013, SING EN STAT 2013
[7]  
Energy Market Company,, PRIC INF UN SING EN
[8]   SHORT-TERM LOAD FORECASTING [J].
GROSS, G ;
GALIANA, FD .
PROCEEDINGS OF THE IEEE, 1987, 75 (12) :1558-1573
[9]   Composite load modeling via measurement approach [J].
He, RM ;
Ma, J ;
Hill, DJ .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) :663-672
[10]   Neural networks for short-term load forecasting: A review and evaluation [J].
Hippert, HS ;
Pedreira, CE ;
Souza, RC .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (01) :44-55