Genetic Algorithms Based Reference Signal Determination for Temperature Control of Residential Buildings

This paper deals with the use of genetic algorithms for the determination of the optimal set-point signals for the control of the temperature in a residential building for which the use of the rooms, that is, the user requirements, are different throughout the day. In particular, the optimization procedure aims at minimizing the overall energy consumption by satisfying, at the same time, the comfort constraints set by the user. Both the case of radiators and fan-coil units are considered. The presence of unoccupied rooms is also addressed. Finally, a comparison between this approach and a Model Predictive Control based one is presented. Simulation results obtained by using TRNSYS software tool demonstrate the effectiveness of the method.

[1]  Andrew Kusiak,et al.  Modeling and optimization of HVAC systems using a dynamic neural network , 2012 .

[2]  Mehmet Karaköse,et al.  Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system , 2009, Expert Syst. Appl..

[3]  Ralph Evins,et al.  Multi-level optimization of building design, energy system sizing and operation , 2015 .

[4]  Ruxu Du,et al.  Design of intelligent comfort control system with human learning and minimum power control strategies , 2008 .

[5]  Antonio Visioli,et al.  Optimal temperature set-point planning for residential buildings , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[6]  Petru-Daniel Morosan,et al.  A dynamic horizon distributed predictive control approach for temperature regulation in multi-zone buildings , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[7]  Andrew Kusiak,et al.  Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance , 2015 .

[8]  Stefano Cordiner,et al.  Energy management in a domestic microgrid by means of model predictive controllers , 2016 .

[9]  Gerardo Maria Mauro,et al.  Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort , 2015 .

[10]  Cristina Becchio,et al.  The role of nearly-zero energy buildings in the transition towards Post-Carbon Cities , 2016 .

[11]  Gerardo Maria Mauro,et al.  A new comprehensive approach for cost-optimal building design integrated with the multi-objective model predictive control of HVAC systems , 2017 .

[12]  Andrew G. Alleyne,et al.  Decentralized predictive thermal control for buildings , 2014 .

[13]  Petru-Daniel Morosan,et al.  Building temperature regulation using a distributed model predictive control , 2010 .

[14]  Nathan Mendes,et al.  Predictive controllers for thermal comfort optimization and energy savings , 2008 .

[15]  Yong Zhang,et al.  Advanced controller auto-tuning and its application in HVAC systems , 2000 .

[16]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[17]  Cecilia R. Aragon,et al.  How People Actually Use Thermostats , 2010 .

[18]  M. Zaheer-uddin,et al.  Neuro-PID tracking control of a discharge air temperature system , 2004 .

[19]  Zheng Qin,et al.  Experimental implementation of whole building MPC with zone based thermal comfort adjustments , 2017 .

[20]  María del Mar Castilla,et al.  An efficient modelling for temperature control of residential buildings , 2016 .

[21]  Christian Ghiaus,et al.  Model Predictive Control of thermal comfort as a benchmark for controller performance , 2014 .

[22]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[23]  Gerardo Maria Mauro,et al.  A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance , 2015 .

[24]  Paolo Maria Congedo,et al.  Cost-optimal design for nearly zero energy office buildings located in warm climates , 2015 .

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

[26]  Colin N. Jones,et al.  On the selection of the most appropriate MPC problem formulation for buildings , 2013 .