This document describes a proposed measuring device that uses artificial intelligence to convert Fanger's thermal comfort formula into a model to predict mean vote (PMV) and percentage of dissatisfaction (PPD). The device aims to formalize Fanger's complex, nonlinear model into a hybrid fuzzy logic structure using a Tagaki Sugeno Kang fuzzy model tuned with a nonlinear regression algorithm. This would allow the model to achieve very small errors and be used in real-time applications to control HVAC systems, providing a proper reference signal. The model structure identification is achieved without complicated optimization procedures. Testing showed the TSK model provides good modeling capability.

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Measuring Device for Human Comfort Sensation by Converting Fanger
Formula Using Applications of Artificial Intelligence
Patent · April 2018
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Raad Z. Homod
Basrah University for Oil and Gas
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Measuring Device for Human Comfort Sensation by Converting
Fanger Formula Using Applications of Artificial Intelligence
Inventor: Raad Z. Homod
Department of Oil and Gas Engineering,
Basra University for Oil and Gas, 61004 Garmat Ali, Basra, Iraq
(Tel: +9647821731696, Email: raadahmood@yahoo.com)

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Measuring Device for Human Comfort Sensation by Converting
Fanger Formula Using Applications of Artificial Intelligence
Abstract:
Previously it was used the temperature to evaluate human comfort, later
be clarified that humidity significantly affected on thermal sensation
comfort. Throughout the study of thermal comfort, it has been shown
that more four factors have an impact on the human sensation, these
factors are relative air velocity, radiant temperature, clothing insulation
and metabolic rate. Model’s Fanger is carried out by implicit empirical
equation based on nonlinear numerical methods, it is difficult to use in
real time application. Therefore, this work devoted to formalize this
model into hybrid layers structure by represented as a fuzzy predicted
mean vote (PMV) and predicted percentage of dissatisfaction (PPD)
model which is regarded as a white-box model. In order to be able to
achieve a very small error using a Tagaki Sugeno Kang (TSK) fuzzy
model and tuned by Gauss-Newton method for nonlinear regression
algorithm. This also significantly reduces the number of iterations and
number of rules further, provides small margin error when compared
with neuro-fuzzy model tuned using the backpropagation algorithm
with its notorious long training time requirement. The main reason for
the models is to obtain a proper reference signal for the heating,
ventilating and air conditioning (HVAC) system. The model is tested
on a wide range of parameter variation. The corresponding results show
that a good modelling capability is achieved without employing any
complicated optimization procedures for structure identification with
the TSK model.
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