A Study on Several Machine Learning Methods for Estimating Cabin Occupant Equivalent Temperature

Diana Hintea, James Brusey, Elena Gaura

2015

Abstract

Occupant comfort oriented Heating, Ventilation and Air Conditioning (HVAC) control rises to the challenge of delivering comfort and reducing the energy budget. Equivalent temperature represents a more accurate predictor for thermal comfort than air temperature in the car cabin environment, as it integrates radiant heat and airflow. Several machine learning methods were investigated with the purpose of creating an estimator of cabin occupant equivalent temperature from sensors throughout the cabin, namely Multiple Linear Regression, MultiLayer Perceptron, Multivariate Adaptive Regression Splines, Radial Basis Function Network, REPTree, K-Nearest Neighbour and Random Forest. Experimental equivalent temperature and cabin data at 25 points was gathered in a variety of environmental conditions. A total of 30 experimental hours were used for training and evaluation of the estimator's performance. Most machine learning tehniques provided a Root Mean Square Error (RMSE) between 1.51 °C and 1.85 °C , while the Radial Basis Function Network performed the worst, with an average RMSE of 3.37 °C . The Multiple Linear Regression had an average RMSE of 1.60 °C over the eight body part equivalent temperatures and also had the fastest processing time, enabling a straightforward real-time implementation in a car's engine control unit.

References

  1. Breiman, L. (2001). Random forests. Technical report, University of California Berkeley.
  2. Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, IT-13.
  3. Demsar, J., Curk, T., Erjavec, A., Gorup, C., Hocevar, T., Milutinovic, M., Mozina, M., Polajnar, M., Toplak, M., Staric, A., Stajdohar, M., Umek, L., Zagar, L., Zbontar, J., Zitnik, M., and Zupan, B. (2013). Orange: Data mining toolbox in python. Journal of Machine Learning Research, 14:2349-2353.
  4. Draper, N. and Smith, H. (1981). Applied Regression Analysis. Wiley.
  5. Friedman, J. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19:1-67.
  6. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. (2009). The weka data mining software: An update. SIGKDD Explorations, 11.
  7. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning. Springer.
  8. Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall.
  9. Hintea, D., Brusey, J., Gaura, E., Beloe, N., and Bridge, D. (2011). Mutual information-based sensor positioning for car cabin comfort control. In Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III, KES'11, pages 483-492.
  10. Hintea, D., Kemp, J., Brusey, J., Gaura, E., and Beloe, N. (2014). Applicability of thermal comfort models to car cabin environments. In International Conference on Informatics in Control, Automation and Robotics (ICINCO), volume 1, pages 769-776.
  11. Quinlan, J. (1986). Introduction of decision trees. Machine Learning, 1:81-106.
  12. Srivastava, A., Oza, N., and Stroeve, J. (2005). Virtual sensors: Using data mining techniques to efficiently estimate remote sensing spectra. IEEE Transactions on Geoscience and Remote Sensing, 43.
  13. van Rossum, G. and Drake, F. (2001). Python Reference Manual. PythonLabs.
  14. Way, M. and Srivastava, A. (2006). Novel methods for predicting photometric redshifts from broadband photometry using virtual sensors. The Astrophysical Journal, 647:102-115.
  15. Wenzel, T., Burnham, K., Blundell, M., and Williams, R. (2007). Kalman filter as a virtual sensor: Applied to automotive stability systems. Transactions of the Institute of Measurement and Control, 29.
  16. Witten, I. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
Download


Paper Citation


in Harvard Style

Hintea D., Brusey J. and Gaura E. (2015). A Study on Several Machine Learning Methods for Estimating Cabin Occupant Equivalent Temperature . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 629-634. DOI: 10.5220/0005573606290634


in Bibtex Style

@conference{icinco15,
author={Diana Hintea and James Brusey and Elena Gaura},
title={A Study on Several Machine Learning Methods for Estimating Cabin Occupant Equivalent Temperature},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={629-634},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005573606290634},
isbn={978-989-758-122-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A Study on Several Machine Learning Methods for Estimating Cabin Occupant Equivalent Temperature
SN - 978-989-758-122-9
AU - Hintea D.
AU - Brusey J.
AU - Gaura E.
PY - 2015
SP - 629
EP - 634
DO - 10.5220/0005573606290634