Title :
Energy-saving analysis of neural network control based on PMV in a ship air conditioning system
Author :
Liu Hongmin ; Tu Shuping
Author_Institution :
Merchant Marine Coll., Shanghai Maritime Univ., Shanghai, China
Abstract :
In this paper, the design of neural network approach is developed aiming at the control of the indoor thermal comfort in air-conditioned cabins. Thermal environment of an air-conditioned ship cabin is simulated and analyzed. From the simulation results, predicted mean vote (PMV) is maintained near zero value with the fluctuation range of ±0.36 under neural network control. As for energy consumption, energy consumption of neural network control is less than that of control strategy based on temperature feedback. 8.5% energy can be saved.
Keywords :
air conditioning; energy consumption; feedback; neurocontrollers; air conditioned cabins; energy consumption; energy saving analysis; neural network control; predicted mean vote; ship air conditioning system; temperature feedback; thermal environment; Artificial neural networks; Atmospheric modeling; Energy consumption; Indexes; Marine vehicles; Mathematical model; Temperature control; PMV; neural network control air-conditioning; ship cabin;
Conference_Titel :
Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
Conference_Location :
Jilin
Print_ISBN :
978-1-61284-719-1
DOI :
10.1109/MEC.2011.6025715
