Prediction of thermal comfort index using type-2 fuzzy neural network

Abstract

Predicted Mean Vote (PMV) is the most widely-used index for evaluating the thermal comfort in buildings. But, this index is calculated through complicated iterations so that it is not suitable for real-time applications. To avoid complicated iterative calculation, this paper presents a prediction model for this index. The proposed model utilizes type-2 fuzzy neural network to approximate the input-output characteristic of the PMV model. To tune the parameters of this type-2 fuzzy neural prediction model, a hybrid algorithm which is a combination of the least square estimate (LSE) method and the back-propagation (BP) algorithm is provided. Finally, simulations are given to verify the effectiveness of the proposed prediction model. © 2012 Springer-Verlag.