Mutual Information-Based Sensor Positioning for Car Cabin Comfort Control
Car cabins are transient, non-uniform thermal environments, both with respect to time and space. Identifying representative locations for the Heating, Ventilation and Air Conditioning (HVAC) system sensors is an open research problem. Common sensor positioning approaches are driven by considerations such as cost or aesthetics, which may impact on the performance/outputs of the HVAC system and thus occupants’ comfort. Based on experimental data, this paper quantifies the spacial-temporal variations in the cabin’s environment by using Mutual Information (MI) as a similarity measure. The overarching aim for the work is to find optimal (but practical) locations for sensors that: i) can produce accurate estimates of temperature at locations where sensors would be difficult to place, such as on an occupant’s face or abdomen and ii) thus, support the development of occupant rather than cabin focused HVAC control algorithms. When applied to experimental data from stable and hot/cold soaking scenarios, the method proposed successfully identified practical sensor locations which estimate face and abdomen temperatures of an occupant with less than 0.7°C and 0.5°C error, respectively.
KeywordsMutual Information Thermal Comfort Sensor Location Fuzzy Neural Network Joint Entropy
Unable to display preview. Download preview PDF.
- 1.Cameron, A., Durrant-Whyte, H.: A bayesian approach to optimal sensor placement. The International Journal of Robotics Research 9 (1990)Google Scholar
- 3.Fanger, P.O.: Thermal Comfort. PhD thesis, Technical University of Denmark (1970)Google Scholar
- 4.Guestrin, C., Krause, A., Singh, A.P.: Near-optimal sensor placements in gaussian processes. In: ICML 2005 Proceedings of the 22nd International Conference on Machine Learning (2005)Google Scholar
- 5.Gutierrez, J.M., Kreinovich, V., Osegueda, R., Ferregut, C., George, M.J.: Maximum entropy approach to optimal sensor placement for aerospace non-destructive testing. Maximum Entropy and Bayesian Methods (1998)Google Scholar
- 6.Huizenga, C., Zhang, H., Duan, T., Arens, E.: An improved multinode model of human physiology and thermal comfort. Building Simulation (1999)Google Scholar
- 10.RadTherm©. ThermoAnalytics Inc., Heat Transfer Analysis Software, http://www.thermoanalytics.com/products/radtherm/index.html (accessed on the April 6, 2011),
- 12.Stephen, E.A., Shnathi, M., Rajalakshmy, P., Melbern Parthido, M.: Application of fuzzy logic in control of thermal comfort. International Journal of Computational and Applied Mathematics 5, 289–300 (2010)Google Scholar
- 13.Torres, J.L., Martin, M.L.: Adaptive control of thermal comfort using neural networks. In: Argentine Symposium on Computing Technology (2008)Google Scholar
- 14.Zhang, H.: Human Thermal Sensation and Comfort in Transient and Non-Uniform Thermal Environments. PhD thesis, University of California, Berkeley (2003)Google Scholar