Application of artificial neural network to predict the optimal start time for heating system in building

被引:170
作者
Yang, IH
Yeo, MS
Kim, KW [1 ]
机构
[1] Seoul Natl Univ, Dept Architecture, Seoul 151742, South Korea
[2] Seoul Natl Univ, Res Inst Engn Sci, Seoul 151742, South Korea
关键词
optimal start time; HVAC; heating system; artificial neural network;
D O I
10.1016/S0196-8904(03)00044-X
中图分类号
O414.1 [热力学];
学科分类号
摘要
The artificial neural network (ANN) approach is a generic technique for mapping non-linear relationships between inputs and outputs without knowing the details of these relationships. This paper presents an application of the ANN in a building control system. The objective of this study is to develop an optimized ANN model to determine the optimal start time for a heating system in a building. For this, programs for predicting the room air temperature and the learning of the ANN model based on back propagation learning were developed, and learning data for various building conditions were collected through program simulation for predicting the room air temperature using systems of experimental design. Then, the optimized ANN model was presented through learning of the ANN, and its performance to determine the optimal start time was evaluated. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:2791 / 2809
页数:19
相关论文
共 17 条
[1]  
[Anonymous], OPTIMAL START STOP A
[2]  
*ASHRAE, 1991, ASHRAE HDB HEAT VENT
[3]  
CURTISS PS, 1992, THESIS U COLORADO
[4]  
FRANK PI, 1990, FUNDAMENTALS HEAT MA, P194
[5]  
JACEK MZ, 1992, ARTIFICIAL NEURAL SY, P210
[6]  
Korea Institute of Construction Technology, 1987, STUD HVAC SYST LIGHT
[7]  
KREIDER JF, 1991, ASHRAE T, P777
[8]   pH thermoreversible hydrogels III:: Synthesis and swelling behaviors of (N-isopropylacrylamide-co-acrylic acid) copolymeric hydrogels [J].
Lee, WF ;
Shieh, CH .
JOURNAL OF POLYMER RESEARCH-TAIWAN, 1999, 6 (01) :41-49
[9]  
LEVERMORE GJ, 1992, E FN SPON, P239
[10]  
MILLR RC, 1991, ASHRAE T