Model-predictive control of mixed-mode buildings with rule extraction

Building and Environment

Volumn 46, Issue 2, 2011, Pages 428-437

  May Ostendorp, Peter ^a   Henze, Gregor P ^a   Corbin, Charles D ^a   Rajagopalan, Balaji ^a   Felsmann, Clemens ^b  

^a UCB 450   (United States)

^b DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Generalized linear model; Logistic regression; Mixed mode buildings; Model predictive control; Particle swarm optimization; Rule extraction

Indexed keywords

GENERALIZED LINEAR MODEL; LOGISTIC REGRESSION; MIXED-MODE BUILDINGS; PARTICLE SWARM; RULE EXTRACTION;

DIRECT DIGITAL CONTROL SYSTEMS; MATHEMATICAL MODELS; MODELS; OFFICE BUILDINGS; PARTICLE SWARM OPTIMIZATION (PSO); PREDICTIVE CONTROL SYSTEMS; REAL TIME SYSTEMS; REGRESSION ANALYSIS;

MODEL PREDICTIVE CONTROL;

BUILDING; CONTROL SYSTEM; COOLING; ENERGY CONSERVATION; NUMERICAL MODEL; OPTIMIZATION; REGRESSION ANALYSIS;

BOULDER; COLORADO; UNITED STATES;

EID: 77957779141     PISSN: 03601323     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.buildenv.2010.08.004     Document Type: Article

References (27)

1. Brager G., Borgeson S., Lee Y. Summary report: control strategies for mixed-mode buildings 2007, Technical Report, Center for the Built Environment, University of California, Berkeley, CA.
2. Torcellini P, Deru M, Griffith B, Pless S, Judkoff R, Crawley D, Lessons learned from field evaluation of six high-performance buildings. In: Proceedings of the ACEEE summer study on energy efficiency in buildings; 2004. p. 1-13.
3. Heiselberg P. Principles of hybrid ventilation 2002, Technical report, Aalborg University, Aalborg, Denmark.
4. Brager G, Baker L. Occupant satisfaction in mixed-mode buildings. In: Proceedings of air conditioning and the low carbon cooling challenge. Windsor, UK; 2008.
5. US DOE EIA, Table E6. Electricity consumption (kWh) intensities by end use for non-mall buildings, Technical Report, US Department of Energy - Energy Information Administration, 2003.
6. Chartered Institution of Building Services Engineers, CIBSE AM10: natural ventilation in non-domestic buildings, Page Bros; 2007.
7. Chartered Institution of Building Services Engineers, CIBSE AM13-2000: mixed mode ventilation, Page Bros., 2000.
8. McConahey E. Mixed mode ventilation: finding the right mix. ASHRAE Journal 2008, 50:36-48.
9. DeDear R., Brager G. Developing an adaptive model of thermal comfort and preference. ASHRAE Transactions 1998, 104:1-18.
10. Henze G., Felsmann C., Knabe G. Evaluation of optimal control for active and passive building thermal storage. International Journal of Thermal Sciences 2004, 173-183.
11. Henze G., Le T., Florita A., Felsmann C. Sensitivity analysis of optimal building thermal mass control. Journal of Solar Energy Engineering 2007.
12. Spindler H., Norford L. Naturally ventilated and mixed-mode buildings-part I: thermal modeling. Building and Environment 2008, 736-749.
13. Spindler H., Norford L. Naturally ventilated and mixed-mode buildings-part II: optimal control. Building and Environment 2008, 750-761.
14. Spindler H. System identification and optimal control for mixed-mode cooling, Ph.D. thesis. Massachusetts Institute of Technology; 2004.
15. The Regents of the University of California, The Board of Trustees of the University of Illinois, EnergyPlus; 2009.
16. Deru M, Griffith B, Torcellini P. Establishing Benchmarks for DOE Commercial Building R&D and Program Evaluation. In Proceedings of the ACEEE summer study on energy efficiency in buildings. Asilomar, CA; 2006. p. 1-12.
17. Bessler F.T., Savic D.A., Walters G.A. Water reservoir control with data mining. Journal of Water Resources Planning and Management 2003, 129:26-34.
18. Wei C., Hsu N. Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system. Journal of Hydrology 2009, 365:213-224.
19. Rijal H., Tuohy P., Humphreys M., Nicol J., Samuel A., Clarke J. Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings 2007, 39:823-836.
20. Rijal H. Development of an adaptive window-opening algorithm to predict the thermal comfort, energy use and overheating in buildings. Journal of Building Performance Simulation 2008, 17-30.
21. Rijal H, Humphreys M, Nicol J. How do the occupants control the temperature in mixed-mode buildings? Predicting the use of passive and active controls. In: Proceedings of air conditioning and the low carbon cooling challenge. Windsor, UK; 2008. p. 1-15.
22. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol. 4, p. 39-43.
23. The Mathworks Inc., MATLAB R2009b, 2009.
24. Regonda S., Rajagopalan B., Clark M. A new method to produce categorical streamflow forecasts. Water Resources Research 2006, 42.
25. Wilks D.S. Statistical methods in the atmospheric sciences 1995, Academic Press.
26. The Regents of the University of California, The Board of Trustees of the University of Illinois, EnergyPlus: Weather Data; 2008.
27. Dodier R., Henze G. Statistical analysis of neural networks as applied to building energy prediction. Journal of Solar Energy Engineering 2004, 126:592.