A study on several machine learning methods for estimating cabin occupant equivalent temperature

    Research output: Chapter in Book/Report/Conference proceedingChapter

    2 Citations (Scopus)

    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 estimating 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 evaluating the estimators' 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.
    Original languageEnglish
    Title of host publicationICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings
    PublisherSciTePress
    Pages629-634
    Volume1
    ISBN (Print)978-989758122-9
    Publication statusPublished - Jul 2015
    EventICINCO 2015 - Colmar, Alsace, France
    Duration: 21 Jul 201523 Jul 2015

    Conference

    ConferenceICINCO 2015
    CountryFrance
    CityAlsace
    Period21/07/1523/07/15

    Bibliographical note

    This paper is not available on the repository

    Keywords

    • Air conditioning
    • Artificial intelligence
    • Budget control
    • Climate control
    • Decision trees
    • Functions
    • Information science
    • Learning algorithms
    • Linear regression
    • Mean square error
    • Nearest neighbor search
    • Parameter estimation
    • Radial basis function networks
    • Real time control
    • Regression analysis
    • Robotics

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  • Cite this

    Hintea, D., Brusey, J., & Gaura, E. (2015). A study on several machine learning methods for estimating cabin occupant equivalent temperature. In ICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings (Vol. 1, pp. 629-634). SciTePress.