Observe, Learn, and Adapt (OLA)—An Algorithm for Energy Management in Smart Homes Using Wireless Sensors and Artificial Intelligence

The need for energy efficient and intelligent systemic solutions, has lead many researchers around the world to investigate and evaluate the existing technologies in order to create solutions that would be adopted for the near future intelligent homes and buildings, aiding in smart grid initiatives. In this paper an algorithm based on the adaptable learning system principles is presented. The proposed algorithm utilizes the adaptable learning system concepts. The Observe, Learn, and Adapt (OLA) algorithm proposed is the result of integration of wireless sensors and artificial intelligence concepts towards the same objective: adding more intelligence to a programmable communicating thermostat (PCT), for better energy management and conservation in smart homes. A house simulator was developed and used as an “expert system shell” to assist in implementation and verification of the OLA algorithm. The role of PCT is to provide consumer with a means to manage and reduce energy use, while accommodating their every day schedules, preferences and needs. In this paper, the actual results of learning and adaptability of a PCT equipped with OLA, as a result of the occupant's pattern/schedule changes, and in general, the overall system improvements with respect to energy consumption and savings are demonstrated via simulation for the zone controlled home equipped with OLA and Knowledge Base, versus a home without zone control, Knowledge Base nor OLA.

[1]  H. T. Mouftah,et al.  Simulation of a house heating system using C# — An energy conservation perspective , 2010, CCECE 2010.

[2]  Albert C. Esterline,et al.  Adaptive learning expert systems , 2000, Proceedings of the IEEE SoutheastCon 2000. 'Preparing for The New Millennium' (Cat. No.00CH37105).

[3]  J. Douglas.  Smart thermostats for comfort and conservation , 1994 .

[4]  Sami Karjalainen,et al.  Thermal comfort and use of thermostats in Finnish homes and offices , 2009 .

[5]  Paul K. Wright,et al.  Design architecture for multi-zone HVAC control systems from existing single-zone systems using wireless sensor networks , 2007, SPIE Micro + Nano Materials, Devices, and Applications.

[6]  L. Delahoche,et al.  The Smart Home Concept : our immediate future , 2006, 2006 1ST IEEE International Conference on E-Learning in Industrial Electronics.

[7]  K. Khakpour,et al.  Knowledge base expert system for tuning PID controllers using wireless technology , 2008, 2008 International Conference on Computer and Communication Engineering.

[8]  A. Meier,et al.  RESIDENTIAL THERMOSTATS: COMFORT CONTROLS IN CALIFORNIA HOMES , 2008 .

[9]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[10]  N. Kushiro,et al.  Practical Solutions for Constructing Ubiquitous Network for Building and home control system , 2007, 2007 Digest of Technical Papers International Conference on Consumer Electronics.

[11]  José L. Poza,et al.  Agent-based distributed architecture for mobile robot control , 2008, Eng. Appl. Artif. Intell..

[12]  H. T. Mouftah,et al.  Synergy of the reinforcement learning and agent-based technique for finding optimal solution in a predefined interval , 2010, SummerSim.

[13]  Jia-Yush Yen,et al.  Development of an intelligent energy management network for building automation , 2004, IEEE Transactions on Automation Science and Engineering.

[14]  Oliver Brdiczka,et al.  Detecting Human Behavior Models From Multimodal Observation in a Smart Home , 2009, IEEE Transactions on Automation Science and Engineering.

[15]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[16]  Hussein Mouftah,et al.  An adaptable system for energy management in intelligent buildings , 2011, 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings.

[17]  Jun-Wei Hsieh,et al.  Building a Remote Supervisory Control Network System for Smart Home Applications , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[18]  E. Lewis,et al.  Wireless Sensor Node hardware: A review , 2008, 2008 IEEE Sensors.

[19]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.