Comparative performance analysis of the artificial-intelligence-based thermal control algorithms for the double-skin building

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 the double-skin buildings. For this, one conventional rule-based and four AI-based algorithms were developed, including artificial neural network (ANN), fuzzy logic (FL), and adaptive neuro fuzzy inference systems (ANFIS), for operating the surface openings of the double skin and the heating system. A numerical computer simulation method incorporating the matrix laboratory (MATLAB) and the transient systems simulation (TRNSYS) software was used for the comparative performance tests. The analysis results revealed that advanced thermal-environment comfort and stability can be provided by the AI-based algorithms. In particular, the FL and ANFIS algorithms were superior to the ANN algorithm in terms of providing better thermal conditions. The ANN-based algorithm, however, proved its potential to be the most energy-efficient and stable strategy among the four AI-based algorithms. It can be concluded that the optimal algorithm can be differently determined according to the major focus of the strategy. If comfortable thermal condition is the principal interest, then the FL or ANFIS algorithm could be the proper solution, and if energy saving for space heating and system operation stability is the main concerns, then the ANN-based algorithm may be applicable.

[1]  S. Iniyan,et al.  Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings , 2010 .

[2]  Jin Woo Moon,et al.  Comparative study of artificial intelligence-based building thermal control methods – Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network , 2011 .

[3]  Gianfranco Rizzo,et al.  The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller , 2004 .

[4]  Myoung-Souk Yeo,et al.  Predictive Control of the Radiant Floor Heating System in Apartment Buildings , 2002 .

[5]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[6]  António E. Ruano,et al.  Neural networks based predictive control for thermal comfort and energy savings in public buildings , 2012 .

[7]  Moncef Krarti,et al.  An Overview of Artificial Intelligence-Based Methods for Building Energy Systems , 2003 .

[8]  H. Ertunç,et al.  Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system , 2008 .

[9]  Jin Woo Moon,et al.  Development and Performance Evaluation of Optimal Control logics for the Two-Position- and Variable-Heating Systems in Double Skin Facade Buildings , 2014 .

[10]  Yu Min Kim,et al.  Contribution of natural ventilation in a double skin envelope to heating load reduction in winter , 2009 .

[11]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[12]  M. M. Gouda,et al.  Thermal comfort based fuzzy logic controller , 2001 .

[13]  Jin Woo Moon,et al.  Optimum Application of Thermal Factors to Artificial Neural Network Models for Improvement of Control Performance in Double Skin-Enveloped Buildings , 2013 .

[14]  Michaël Kummert,et al.  A neural network controller for hydronic heating systems of solar buildings , 2004, Neural Networks.

[15]  Jin Yang,et al.  On-line building energy prediction using adaptive artificial neural networks , 2005 .

[16]  Jin Woo Moon,et al.  Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes , 2014 .

[17]  Jin Woo Moon.  Integrated control of the cooling system and surface openings using the artificial neural networks , 2015 .

[18]  Nicolas Morel,et al.  NEUROBAT, A PREDICTIVE AND ADAPTIVE HEATING CONTROL SYSTEM USING ARTIFICIAL NEURAL NETWORKS , 2001 .

[19]  Servet Soyguder,et al.  Predicting of fan speed for energy saving in HVAC system based on adaptive network based fuzzy inference system , 2009, Expert Syst. Appl..

[20]  Ji-Hyun Lee,et al.  Preliminary performance tests on artificial neural network models for opening strategies of double skin envelopes in winter , 2014 .

[21]  D. Kolokotsa,et al.  Comparison of the performance of fuzzy controllers for the management of the indoor environment , 2003 .

[22]  Constantinos A. Balaras,et al.  Development of a neural network heating controller for solar buildings , 2000, Neural Networks.

[23]  Ji-Hyun Lee,et al.  Performance evaluation of artificial neural network-based variable control logic for double skin enveloped buildings during the heating season , 2014 .

[24]  Servet Soyguder,et al.  An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach , 2009 .

[25]  Anastasios I. Dounis,et al.  Design of a fuzzy set environment comfort system , 1995 .

[26]  Abdullatif Ben-Nakhi,et al.  Energy conservation in buildings through efficient A/C control using neural networks , 2002 .

[27]  Myoung-Souk Yeo,et al.  Application of artificial neural network to predict the optimal start time for heating system in building , 2003 .

[28]  André De Herde,et al.  Are energy consumptions decreased with the addition of a double-skin? , 2007 .

[29]  M. M. Gouda,et al.  Quasi-adaptive fuzzy heating control of solar buildings , 2006 .

[30]  Jin Woo Moon,et al.  Development of an artificial neural network model based thermal control logic for double skin envelopes in winter , 2013 .