Forecasting building occupancy using sensor network data

Proc. of 2nd Int. Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine 2013 - Held in Conj. with SIGKDD 2013 Conf.

Volumn , Issue , 2013, Pages 87-94

  Howard, James ^a   Hoff, William ^a  

^a Department of Metallurgical and Materials Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUILDINGS; COOLING SYSTEMS; DATA MINING; FORECASTING;

BAYESIAN; BUILDING OCCUPANCY; FORECASTING MODELS; HEATING AND COOLING SYSTEMS; INFRARED MOTION SENSORS; NETWORK DATA; PASSIVE INFRARED; SENSORS NETWORK; SIMPLE++; TIME-SERIES DATA;

SENSOR NETWORKS;

EID: 84890593985     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2501221.2501233     Document Type: Conference Paper

References (20)

1. Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, and T. Weng. Occupancy-driven energy management for smart building automation. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys '10, page 1, New York, New York, USA, 2010. ACM Press.
2. G. Box, G. Jenkings, and G. Reinsel. Time Series Analysis: Forecasting and Control. John Wiley & Sons, Inc., 4th edition, 2008.
3. F. Diebold. A Note on Bayesian Forecast Combination Procedures. In A. H. Westlund and P. Hackl, editors, Economic Structural Change: Analysis and Forecasting, pages 225{232. Springer-Verlag, 1991.
4. U. DOE. Building Energy Databook, 2010.
5. R. Goldstein, A. Tessier, and A. Khan. Schedule-calibrated occupant behavior simulation. In Proceedings of the 2010 Spring Simulation Multiconference on - SpringSim '10, page 1, New York, New York, USA, 2010. ACM Press.
6. J. Hansen and R. Nelson. Forecasting and recombining time-series compents by using neural networks. Joural of the Operational Research Society, 54:307{317, 2003.
7. W. A. Hoff and J. W. Howard. Activity recognition in a dense sensor network. In 1st International Conference on Sensor Networks and Applications (SNA2009), page 6, 2009.
8. W.-C. Hong, Y. Dong, F. Zheng, and S. Y. Wei. Hybrid evolutionary algorithms in a SVR traffic ow forecasting model. Applied Mathematics and Computation, 217(15):6733{6747, Apr. 2011.
9. C. Hsu, C. Chang, and C. Lin. A practical guide to support vector classification. (1):1{16, 2003.
10. P. Li, M. Barić, S. Narayanan, and S. Yuan. A Simulation-Based Study of Model Predictive Control in a Medium-Sized Commercial Building. In International High Performance Buildings Conference, pages 1{10, 2012.
11. G. M. Ljung and G. E. P. Box. On a Measure of a Lack of Fit in Time Series Models. Biometrika, 65(2):297{303, 1978.
12. Y. Ma, F. Borrelli, and B. Hencey. Model predictive control for the operation of building cooling systems. In America Control Conference, 2010.
13. S. Mamidi, Y. Chang, and R. Maheswaran. Improving building energy efficiency with a network of sensing, learning and prediction agents. In International Conference on Autonomous Agents and Multi Agent Systems. AAMAS 2012, 2012.
14. G. Newsham and B. Birt. Building-level occupancy data to improve ARIMA-based electricity use forecasts. In 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys 2010), 2010.
15. J. Page, D. Robinson, N. Morel, and J.-L. Scartezzini. A generalised stochastich model for the simulation of occupant presence. Energy and Buildings, 40:83{98, 2008.
16. V. Petridis, A. Kehagias, L. Petrou, A. Bakirtzis, S. Kiartzis, H. Panagiotou, and N. Maslaris. A Bayesian multiple models combination method for time series prediction. Journal of intelligent and robotic systems, 31(1):69{89, 2001.
17. B. M. Williams and L. a. Hoel. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering, 129(6):664, 2003.
18. C. Wren, Y. Ivanov, D. Leigh, and J. Westbues. The MERL motion detector dataset: 2007 workshop on massive datasets. In ICMI Workshop on Massive Datasets, 2007.
19. C.-H. Wu, J.-M. Ho, and D. Lee. Travel-Time Prediction With Support Vector Regression. IEEE Transactions on Intelligent Transportation Systems, 5(4):276{281, Dec. 2004.
20. W. Zheng, D.-H. Lee, and Q. Shi. Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach. Journal of Transportation Engineering, 132(2):114, 2006.