Comparing occupancy models and data mining approaches for regular occupancy prediction in commercial buildings

被引:15
作者
Chen, Zhenghua [1 ]
Soh, Yeng Chai [1 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, 50 Nanyang Ave, Singapore, Singapore
基金
新加坡国家研究基金会;
关键词
building occupancy prediction; occupancy models; data mining approaches; time horizons; SUPPORT VECTOR MACHINES; ENERGY-CONSUMPTION; INFORMATION; SIMULATION; BEHAVIOR; SENSORS;
D O I
10.1080/19401493.2016.1199735
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Occupancy information can help us to achieve high energy-efficient buildings. Previous works mainly focus on predicting the presence and absence of occupants in homes or single person offices. We attempt to predict regular occupancy level in a commercial building deployment scenario. The occupancy prediction models can be divided into two categories of occupancy models and data mining approaches. For the occupancy models, we shall investigate the efficiencies of two widely used multi-occupant models, that is, inhomogeneous Markov chain and multivariate Gaussian. For the data mining approaches, we propose the application of autoregressive integrated moving average, artificial neural network and support vector regression. Experiments have been conducted using actual occupancy data under four different prediction horizons, that is, 15min, 30min, 1 and 2h. The results demonstrated a guideline in how to choose a proper method for the prediction of occupancy in commercial buildings under different prediction horizons.
引用
收藏
页码:545 / 553
页数:9
相关论文
共 37 条
[1]  
[Anonymous], TECHNICAL REPORT
[2]   Support vector machine with adaptive parameters in financial time series forecasting [J].
Cao, LJ ;
Tay, FEH .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2003, 14 (06) :1506-1518
[3]   Modeling regular occupancy in commercial buildings using stochastic models [J].
Chen, Zhenghua ;
Xu, Jinming ;
Soh, Yeng Chai .
ENERGY AND BUILDINGS, 2015, 103 :216-223
[4]   ARIMA models to predict next-day electricity prices [J].
Contreras, J ;
Espínola, R ;
Nogales, FJ ;
Conejo, AJ .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2003, 18 (03) :1014-1020
[5]   Occupancy schedules learning process through a data mining framework [J].
D'Oca, Simona ;
Hong, Tianzhen .
ENERGY AND BUILDINGS, 2015, 88 :395-408
[6]   Using artificial neural networks to predict the impact of daylighting on building final electric energy requirements [J].
da Fonseca, Raphaela Walger ;
Didone, Evelise Leite ;
Ruttkay Pereira, Fernando Oscar .
ENERGY AND BUILDINGS, 2013, 61 :31-38
[7]   People occupancy detection and profiling with 3D depth sensors for building energy management [J].
Diraco, Giovanni ;
Leone, Alessandro ;
Siciliano, Pietro .
ENERGY AND BUILDINGS, 2015, 92 :246-266
[8]   Building occupancy detection through sensor belief networks [J].
Dodier, Robert H. ;
Henze, Gregor P. ;
Tiller, Dale K. ;
Guo, Xin .
ENERGY AND BUILDINGS, 2006, 38 (09) :1033-1043
[9]   Applying support vector machines to predict building energy consumption in tropical region [J].
Dong, B ;
Cao, C ;
Lee, SE .
ENERGY AND BUILDINGS, 2005, 37 (05) :545-553
[10]   An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network [J].
Dong, Bing ;
Andrews, Burton ;
Lam, Khee Poh ;
Hoeynck, Michael ;
Zhang, Rui ;
Chiou, Yun-Shang ;
Benitez, Diego .
ENERGY AND BUILDINGS, 2010, 42 (07) :1038-1046