Providing indoor thermal comfort and saving energy are two main goals of indoor environmental control system. An intelligent comfort control system by combining the intelligent control and minimum power control strategies for the indoor environment is presented in this paper. In the system, for realizing the comfort control, the predicted mean vote (PMV) is designed as the control goal, and with chastening formulas of PMV, it is controlled to optimize for improving indoor comfort lever by considering six comfort related variables. On the other hand, a RBF neural network based on genetic algorithm is designed to calculate PMV for better performance and overcoming the nonlinear feature of the PMV calculation better. The formulas given in the paper are presented for calculating the expected output values basing on the input samples, and the RBF network model is trained depending on input samples and the expected output values. The simulation result is proved that the design of the intelligent calculation method is valid. Moreover, this method has a lot of advancements such as high precision, fast dynamic response and good system performance are reached, it can be used in practice with requested calculating error.

Date Published: 20 March 2013
PDF: 7 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87680G (20 March 2013); doi: 10.1117/12.2010622

Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)