# Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth

### Carlo Manna, Nic Wilson, Kenneth N. Brown

#### Abstract

A personalized thermal comfort prediction method is proposed for use in combination with smart controls for building automation. Occupant thermal comfort is traditionally measured and predicted by the Predicted Mean Vote (PMV) metric, which is based on extensive field trials linking reported comfort levels with the various factors. However, PMV is a statistical measure applying to large populations, and the actual thermal comfort could be significantly different from the predicted value for small groups of people. Moreover it may be hard to use for a real-time controller due to the number of sensor readings needed. In the present paper, we propose Robust Locally Weighted Regression with Adaptive Bandwidth (LRAB), a kernel based method, to learn individual occupant thermal comfort based on historical reports. Using publicly available datasets, we demonstrate that this technique is significantly more accurate in predicting individual comfort than PMV and other kernel methods. Therefore, is a promising technique to be used as input to adpative HVAC control systems.

#### References

- Ari, S., Wilcoxen, P., Khalifa, H., Dannenhoffer, J., and Isik, C. (2008). A practical approach to individual thermal comfort and energy optimization problem. In Fuzzy Information Processing Society, 2008. NAFIPS 2008. Annual Meeting of the North American, pages 1 -6.
- ASHRAE (2010). ASHRAE Standard: Thermal Environmental Conditions for Human Occupancy. ASHRAE.
- Atthajariyakul, S. and Leephakpreeda, T. (2005). Neural computing thermal comfort index for HVAC systems.
- Bingxin, M., Jiong, S., and Yanchao, W. (2011). Experimental design and the GA-BP prediction of human thermal comfort index. In Natural Computation (ICNC), 2011 Seventh International Conference on, volume 2, pages 771 -775.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning.
- Busch, J. F. (1992). A tale of two populations: thermal comfort in air-conditioned and naturally ventilated offices in Thailand. Energy and Buildings, 18(3-4):235 - 249.
- Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368):pp. 829-836.
- Cleveland, W. S. and Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403):pp. 596-610.
- Daum, D., Haldi, F., and Morel, N. (2011). A personalized measure of thermal comfort for building controls. Building and Environment, 46(1):3-11.
- de Dear, R. and Schiller Brager, G. (2001). The adaptive model of thermal comfort and energy conservation in the built environment. International Journal of Biometeorology, 45:100-108.
- de Dear, R. J. and Brager, G. S. (2002). Thermal comfort in naturally ventilated buildings: revisions to ashrae standard 55. Energy and Buildings, 34(6):549 - 561.
- Fanger, P. (1972). Thermal comfort: analysis and applications in environmental engineering. McGraw-Hill, New York.
- Federspiel, C. C. and Asada, H. (1992). User-adaptable comfort control for HVAC systems. In American Control Conference, 1992, pages 2312 -2319.
- Feldmeier, M. and Paradiso, J. A. (2010). Personalized hvac control system. In In Internet of Things 2010 Conference.
- Freire, R. Z., Oliveira, G. H., and Mendes, N. (2008). Predictive controllers for thermal comfort optimization and energy savings. Energy and Buildings, 40(7):1353 - 1365.
- Gagge, A. P., Fobelets, A. P., and Berglund, L. G. (1986). A standard predictive index of human response to the thermal environment.
- Hastie, T., Tibshirani, R., and Friedman, J. H. (2003). The Elements of Statistical Learning. Springer, corrected edition.
- Humphreys, M. and Nicol, J. (2000). Effects of measurement and formulation error on thermal comfort indices in the ashrae database of field studies. ASHRAE transactions, 106:493-502.
- Humphreys, M. A. and Nicol, J. F. (2002). The validity of iso-pmv for predicting comfort votes in everyday thermal environments. Energy and Buildings, 34(6):667 - 684.
- ISO (1994). ISO 7730: Moderate Thermal EnvironmentsDetermination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort. ISO.
- Kumar, S. and Mahdavi, A. (2001). Integrating thermal comfort field data analysis in a case-based building simulation environment. Building and Environment, 36(6):711 - 720.
- Olesen, B. W. (2004). International standards for the indoor environment. Indoor Air, 14:18-26.
- Schumann, A., Wilson, N., and Burillo, M. (2010). Learning user preferences to maximise occupant comfort in office buildings. In Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I, IEA/AIE'10, pages 681-690, Berlin, Heidelberg. Springer-Verlag.
- Sherman, M. (1985). A simplified model of thermal comfort. Energy and Buildings, 8(1):37 - 50.
- Shukor, S. A. A., Kohlhof, K., and Jamal, Z. A. Z. (2007). Development of a pmv-based thermal comfort modelling. In Proceedings of the 18th IASTED International Conference: modelling and simulation, MOAS'07, pages 670-675, Anaheim, CA, USA. ACTA Press.
- Wilson, N. (2012). On balancing occupants' comfort in shared spaces. In Proc. 6th Multidisciplinary Workshop on Advances in Preference Handling (MPREF12).
- Yang, K. and Su, C. (1997). An approach to building energy savings using the PMV index. Building and Environment, 32(1):25 - 30.
- Yang, W. and Zhang, G. (2008). Thermal comfort in naturally ventilated and air-conditioned buildings in humid subtropical climate zone in china. International Journal of Biometeorology, 52:385-398.

#### Paper Citation

#### in Harvard Style

Manna C., Wilson N. and Brown K. (2013). **Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth** . In *Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,* ISBN 978-989-8565-55-6, pages 32-40. DOI: 10.5220/0004375100320040

#### in Bibtex Style

@conference{smartgreens13,

author={Carlo Manna and Nic Wilson and Kenneth N. Brown},

title={Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth},

booktitle={Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,},

year={2013},

pages={32-40},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0004375100320040},

isbn={978-989-8565-55-6},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,

TI - Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth

SN - 978-989-8565-55-6

AU - Manna C.

AU - Wilson N.

AU - Brown K.

PY - 2013

SP - 32

EP - 40

DO - 10.5220/0004375100320040