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

fYear

2011

fDate

19-22 Aug. 2011

Firstpage

1331

Lastpage

1334

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on

Conference_Location

Jilin

Print_ISBN

978-1-61284-719-1

Type

conf

DOI

10.1109/MEC.2011.6025715

Filename

6025715