Predictive Thermal Comfort Control for Cyber-Physical Home Systems

With the emergence of Internet of Things (IoT) and smart homes, the demand for energy efficient thermal comfort has also increased significantly to address the importance of quality of life (QoL) in a modern society. In this paper, we present a model predictive control (MPC) based thermal comfort controller for cyber-physical home systems (CPHS). The MPC controller is integrated into the existing Energy Efficient Thermal Comfort Control (EETCC) system that was developed for the experimental smart house, iHouse. The advantages of MPC was explored in a real time manner for reference tracking and energy minimization scenarios. Besides, Predicted Mean Vote (PMV) index is also adopted into the MPC controller to further enhance the energy efficiency and thermal comfort of the CPHS. The proposed methods are evaluated and verified under various seasons in a CPHS simulation using raw environmental data from the iHouse.

[1]  M. Sagi,et al.  Smart home automation , 2012, 2012 20th Telecommunications Forum (TELFOR).

[2]  Seyed Hossein Sadati,et al.  Control Techniques in Heating, Ventilating and Air Conditioning (HVAC) Systems , 2008 .

[3]  Farrokh Janabi-Sharifi,et al.  Theory and applications of HVAC control systems – A review of model predictive control (MPC) , 2014 .

[4]  R. Neves-Silva,et al.  Distributed MPC for thermal house comfort with shifting loads and limited energy resources , 2013, 2013 International Conference on Renewable Energy Research and Applications (ICRERA).

[5]  Gionata Cimini,et al.  Indoor thermal comfort control through fuzzy logic PMV optimization , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

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

[7]  María del Mar Castilla,et al.  An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building , 2017 .

[8]  Azman Osman Lim,et al.  PID Controller for Temperature Control with Multiple Actuators in Cyber-Physical Home System , 2012, 2012 15th International Conference on Network-Based Information Systems.

[9]  Carl-Eric Hagentoft,et al.  Application of model based predictive control for water-based floor heating in low energy residential buildings , 2011 .

[10]  Lukas Ferkl,et al.  Optimization of Predicted Mean Vote index within Model Predictive Control framework: Computationally tractable solution , 2012 .

[11]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[12]  Azman Osman Lim,et al.  Energy efficient thermal comfort control for cyber-physical home system , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[13]  Peng Xu,et al.  Demand reduction in building energy systems based on economic model predictive control , 2012 .

[14]  Charlie Wilson,et al.  Benefits and risks of smart home technologies , 2017 .

[15]  David H. Albonesi,et al.  Energy-comfort optimization using discomfort history and probabilistic occupancy prediction , 2014, International Green Computing Conference.

[16]  George Stavrakakis,et al.  PID controller tuning and implementation aspects for building thermal control , 2006 .

[17]  Rayner Alfred,et al.  Implementation of Energy Efficient Thermal Comfort Control for Cyber-Physical Home Systems , 2017 .

[18]  Tan Yasuo,et al.  Simulation of Solar Photovoltaic and Fuel Cell Energy System for Smart Community Simulator , 2017 .

[19]  Francisco Rodríguez,et al.  A comparison of thermal comfort predictive control strategies , 2011 .

[20]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[21]  Azman Osman Lim,et al.  Building High-Accuracy Thermal Simulation for Evaluation of Thermal Comfort in Real Houses , 2013, ICOST.

[22]  Liyanage C. De Silva,et al.  State of the art of smart homes , 2012, Eng. Appl. Artif. Intell..