Published March 5, 2015 | Version v1
Journal article

Integrated control of the cooling system and surface openings using the artificial neural networks

Creators

Description

This study aimed at suggesting an indoor temperature control method that can provide a comfortable thermal environment through the integrated control of the cooling system and the surface openings. Four control logic were developed, employing different application levels of rules and artificial neural network models. Rule-based control methods represented the conventional approach while ANN-based methods were applied for the predictive and adaptive controls. Comparative performance tests for the conventional- and ANN-based methods were numerically conducted for the double-skin-facade building, using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) software, after proving the validity by comparing the simulation and field measurement results. Analysis revealed that the ANN-based controls of the cooling system and surface openings improved the indoor temperature conditions with increased comfortable temperature periods and decreased standard deviation of the indoor temperature from the center of the comfortable range. In addition, the proposed ANN-based logic effectively reduced the number of operating condition changes of the cooling system and surface openings, which can prevent system failure. The ANN-based logic, however, did not show superiority in energy efficiency over the conventional logic. Instead, they have increased the amount of heat removal by the cooling system. From the analysis, it can be concluded that the ANN-based temperature control logic was able to keep the indoor temperature more comfortably and stably within the comfortable range due to its predictive and adaptive features. - Highlights: • Integrated rule-based and artificial neural network based logics were developed. • A cooling device and surface openings were controlled in an integrated manner. • Computer simulation method was employed for comparative performance tests. • ANN-based logics showed the advanced features of thermal environment. • Rule-based logics showed the better energy efficiency for space cooling

Availability note (English)

Available from http://dx.doi.org/10.1016/j.applthermaleng.2014.12.058

Additional details

Identifiers

DOI
10.1016/j.applthermaleng.2014.12.058;
PII
S1359-4311(14)01187-9;

Publishing Information

Journal Title
Applied Thermal Engineering
Journal Volume
78
Journal Page Range
p. 150-161
ISSN
1359-4311
CODEN
ATENFT

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
47015196
Subject category
S42: ENGINEERING;
Descriptors DEI
AIR CONDITIONING; COMPARATIVE EVALUATIONS; COMPUTERIZED SIMULATION; COOLING; COOLING SYSTEMS; ENERGY EFFICIENCY; FAILURES; HEAT TRANSFER; M CODES; NEURAL NETWORKS; SURFACES; T CODES; TEMPERATURE CONTROL; THERMAL COMFORT
Descriptors DEC
COMPUTER CODES; CONTROL; EFFICIENCY; ENERGY SYSTEMS; ENERGY TRANSFER; EVALUATION; SIMULATION

Optional Information

Copyright
Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.