이중외피 건물의 난방시스템과 개구부의 통합제어를 위한 규칙기반제어와 인공신경망제어의 성능비교

This study aimed at numerically comparing two integrated logics for controlling a heating device and openings of double skin facade buildings. The completely conventional rule-based control strategy and the artificial-neural-network-based strategy were developed. In particular, the artificial neural network models were applied for predictive and adaptive control of the heating system and the openings of the double skin facades. Comparative performance tests for the developed logics were conducted using the computer simulation method such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis revealed that the artificial neural network-based temperature control logics for the double skin facade buildings provided more stable temperature conditions.