Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building

References

• Bauer, E. , and Kohavi, R. , 1999. An empirical comparison of voting classification algorithms: bagging, boosting, and variants , Machine Learning 36 (1) (1999), pp. 105–139, (doi:10.1023/A:1007515423169).
   Web of Science ®Google Scholar
• Bessler, F. T. , Savic, D. A. , and Walters, G. A. , 2003. Water reservoir control with data mining , Journal of Water Resources Planning and Management 129 (1) (2003), pp. 26–34, (doi:10.1061/(ASCE)0733-9496(2003)129:1(26)).
   Web of Science ®Google Scholar
• Brager, G. S. , and Baker, L. , 2008. Occupant satisfaction in mixed-mode buildings . Presented at Proceedings of Air Conditioning and the Low Carbon Cooling Challenge.
   Google Scholar
• Brambley, M. R. , et al., 2005. Advanced sensors and controls for building applications: market assessment and potential R & D pathways . Richland, Washington: Pacific Northwest National Laboratory; 2005, Technical report.
   Google Scholar
• Braun, J. , 1990. Reducing energy costs and peak electrical demand through optimal control of building thermal storage , ASHRAE Transactions 96 (2) (1990), pp. 876–888.
   Google Scholar
• Braun, J. , 1996. A simplified method for determining optimal cooling control strategies for thermal storage in building mass , HVAC&R Research 2 (1) (1996), pp. 59–78.
   Google Scholar
• Breiman, L. , 1984. Classification and regression trees . London: Wadsworth International Group; 1984.
   Google Scholar
• Chartered Institution of Building Services Engineers, 2000. CIBSE AM13: mixed mode ventilation . London: Page Bros; 2000.
   Google Scholar
• Chartered Institution of Building Services Engineers, 2007. CIBSE AM10: Natural ventilation in non-domestic buildings . London: Page Bros; 2007.
   Google Scholar
• Coffey, B. , 2011. Using building simulation and optimization to calculate lookup tables for control . Berkeley: Department of Architecture, University of California; 2011, Thesis (PhD).
   Google Scholar
• Deru, M. , Griffith, B. , and Torcellini, P. , 2006. Establishing benchmarks for DOE commercial building R&D and program evaluation . Presented at Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings.
   Google Scholar
• EnergyPlus Development Team, 2011. EnergyPlus v6.0 . Berkeley, CA: The Regents of the University of California and The Board of Trustees of the University of Illinois; 2011.
   Google Scholar
• Freund, Y. , Schapire, R. , and Vitányi, P. , 1995. A desicion-theoretic generalization of on-line learning and anapplication to boosting , Computational Learning Theory – Lecture Notes in Computer Science 904 (1995), pp. 23–37, (doi:10.1007/3-540-59119-2_166).
   Google Scholar
• Friedman, J. , 2000. Additive logistic regression: A statistical view of boosting , Annals of statistics 28 (2) (2000), pp. 337–374, (doi:10.1214/aos/1016218223).
   Web of Science ®Google Scholar
• Gill, J. , 2001. Generalized linear models: a unified approach . Thousand Oaks, CA: SAGE Publications Inc; 2001.
   Google Scholar
• Heiselberg, P. , 2002. Principles of hybrid ventilation . Denmark: Aalborg University, Aalborg; 2002, Technical report.
   Google Scholar
• Henze, G. P. , Felsmann, C. , and Knabe, G. , 2004. Evaluation of optimal control for active and passive building thermal storage , International Journal of Thermal Sciences 43 (2) (2004), pp. 173–183, (doi:10.1016/j.ijthermalsci.2003.06.001).
   Google Scholar
• Henze, G. P. , et al., 2007. Sensitivity analysis of optimal building thermal mass control , Journal of Solar Energy Engineering 129 (4) (2007), pp. 473–485, (doi:10.1115/1.2770755).
   Web of Science ®Google Scholar
• Kennedy, J. , and Eberhart, R. , 1995. Particle swarm optimization . Presented at Proceedings of the IEEE International Conference on Neural Networks.
   Google Scholar
• May-Ostendorp, P. , et al., 2011. Model-predictive control of mixed-mode buildings with rule extraction , Building and Environment 46 (2) (2011), pp. 428–437, (doi:10.1016/j.buildenv.2010.08.004).
   Web of Science ®Google Scholar
• McConahey, E. , 2008. Mixed mode ventilation: Finding the right mix , ASHRAE Journal 50 (9) (2008), pp. 36–48.
   Web of Science ®Google Scholar
• Mills, E. , 2009. Building commissioning: a golden opportunity for reducing energy costs and greenhouse gas emissions . Berkeley, CA: Lawrence Berkeley National Laboratory; 2009, Technical report.
   Google Scholar
• R Development Core Team, 2011. R: a language and environment for statistical computing . Vienna, Austria: R Foundation for Statistical Computing; 2011.
   Google Scholar
• Regonda, S. K. , Rajagopalan, B. , and Clark, M. , 2006. A new method to produce categorical streamflow forecasts , Water Resources Research 42 (9) (2006), p. W09501.
   Web of Science ®Google Scholar
• Spindler, H. C. , 2004. System identification and optimal control for mixed-mode cooling . Massachusetts Institute of Technology; 2004, Thesis (PhD).
   Google Scholar
• Spindler, H. C. , and Norford, L. K. , 2008. Naturally ventilated and mixed-mode buildings – part II: optimal control , Building and Environment 44 (4) (2008), pp. 750–761, (doi:10.1016/j.buildenv.2008.05.018).
   Web of Science ®Google Scholar
• Spindler, H. C. , and Norford, L. K. , 2009. Naturally ventilated and mixed-mode buildings – part I: Thermal modeling , Building and Environment 44 (4) (2009), pp. 736–749, (doi:10.1016/j.buildenv.2008.05.019).
   Web of Science ®Google Scholar
• The Mathworks Inc, 2011. MATLAB R2011b . Natick, MA: The Mathworks Inc; 2011.
   Google Scholar
• Torcellini, P. A. , et al., 2004. Lessons learned from field evaluation of six high-performance buildings . Presented at Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings, July.
   Google Scholar
• US Energy Information Administration, 2010. Annual Energy Review 2010 . Washington, DC: US Energy Information Administration; 2010, Technical report.
   Google Scholar
• Wei, C. C. , and Hsu, N. S. , 2009. Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system , Journal of Hydrology 365 (3–4) (2009), pp. 213–224, (doi:10.1016/j.jhydrol.2008.11.038).
   Web of Science ®Google Scholar
• Wilks, D. S. , 1995. Statistical methods in the atmospheric sciences . San Diego, CA: Academic Press; 1995.
   Google Scholar