Abstract For building, “surroundings” that effect on indoor-air condition change with respect to the time. Without proper determination of the desired indoor-air condition to heating, ventilating and air-conditioning (HVAC) system, it may not be feasible to provide simultaneously occupants with thermal comfort and acceptable air quality with efficient energy consumption all the time. This paper presents an alternative methodology of real-time determination of optimal indoor-air condition for HVAC system in order to achieve such total requirements. Predicted mean vote (PMV), CO 2 concentration and cooling/heating load are used as parameter indices for thermal comfort, indoor-air quality and energy consumption respectively. The performance index of the HVAC system is then defined by summation in terms of square errors between those actual parameter indices and their desired values. This performance index is to be systematically minimized by a gradient-based technique in order to yield optimal indoor-air condition for HVAC system. A case study was chosen in 24 h operating HVAC system of a single-storey building by determining indoor-air temperature, indoor-air humidity, indoor-air velocity, and air-ventilation rate. The experiment results show that the proposed methodology can be efficiently implemented in the real-time determination of indoor-air condition to HVAC system that maintains PMV and CO 2 concentration close to the desired levels with less energy consumption when compared to those from the conventional approach.
[1]
P. O. Fanger,et al.
Thermal comfort: analysis and applications in environmental engineering,
,
1972
.
[2]
Edward Arens,et al.
Air movement and thermal comfort
,
1993
.
[3]
B. F. Warren,et al.
Demand controlled ventilation by room CO2 concentration: a comparison of simulated energy savings in an auditorium space
,
1991
.
[4]
A.T.P. So,et al.
Implementation of comfort-based air-handling unit control algorithms
,
2000
.
[5]
Thananchai Leephakpreeda.
Adaptive Fuzzy Logic Controllers with Sensitivity Learning Rules
,
1999
.
[6]
Thananchai Leephakpreeda.
Resolving scaling problem of wide-range data in neural network training with fuzzy linguistic model
,
2001
.
[7]
Jerald D. Parker,et al.
Heating, Ventilating, and Air Conditioning: Analysis and Design
,
1977
.
[8]
K. Yang,et al.
AN APPROACH TO BUILDING ENERGY SAVINGS USING THE PMV INDEX
,
1997
.