Comparative performance analysis of the artificial-intelligence-based thermal control algorithms for the double-skin building
作者： Jin Woo Moon
DOI: 10.1016/J.APPLTHERMALENG.2015.08.038
关键词: Systems simulation 、 Algorithm 、 Optimal control 、 Artificial neural network 、 Adaptive neuro fuzzy inference system 、 Fuzzy logic 、 TRNSYS 、 Control engineering 、 Heating system 、 Engineering 、 Neuro-fuzzy
摘要: Abstract This study aimed at developing artificial-intelligence-(AI)-theory-based optimal control algorithms for improving the indoor temperature conditions and heating energy efficiency of double-skin buildings. For this, one conventional rule-based four AI-based were developed, including artificial neural network (ANN), fuzzy logic (FL), adaptive neuro inference systems (ANFIS), operating surface openings double skin system. A numerical computer simulation method incorporating matrix laboratory (MATLAB) transient (TRNSYS) software was used comparative performance tests. The analysis results revealed that advanced thermal-environment comfort stability can be provided by algorithms. In particular, FL ANFIS superior to ANN algorithm in terms providing better thermal conditions. ANN-based algorithm, however, proved its potential most energy-efficient stable strategy among It concluded differently determined according major focus strategy. If comfortable condition is principal interest, then or could proper solution, if saving space system operation main concerns, may applicable.
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