A low-complexity control mechanism targeting smart thermostats

Abstract This paper introduces a low-cost, high-quality Decision Making Mechanism for supporting the tasks of temperature regulation of existing HVAC installations in a smart building environment. It incorporates Artificial Neural Networks and Fuzzy Logic in order to improve the occupants’ thermal comfort while maintaining the total energy consumption. Contrary to existing approaches, it focuses in achieving significantly low computational complexity, which in turn enables its hardware implementation onto low-cost embedded platforms, such the ones used in smart thermostats. Both the software components and hardware implantation are described in detail. To demonstrate its effectiveness, the proposed method was compared to ruled-based controllers, as well as state-of-the-art control techniques. A simulation model was developed using the EnergyPlus building simulation suite, a detailed modeled micro-grid environment of buildings located in Chania Greece and historic weather and energy pricing data. Simulation results validate the effectiveness of our approach.

[1]  Iakovos Michailidis,et al.  Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule , 2015 .

[2]  Joseph Andrew Clarke,et al.  The role of simulation in support of Internet-based energy services , 2004 .

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

[4]  J. Doyle,et al.  𝓗∞ Control of Nonlinear Systems: a Convex Characterization , 1995, IEEE Trans. Autom. Control..

[5]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.

[6]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[7]  H. N. Lam,et al.  Using genetic algorithms to optimize controller parameters for HVAC systems , 1997 .

[8]  Francisco Herrera,et al.  A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems , 2005, Eng. Appl. Artif. Intell..

[9]  Zhen-Jiang Zhang,et al.  A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks , 2015, J. Sensors.

[10]  K. F. Fong,et al.  HVAC system optimization for energy management by evolutionary programming , 2006 .

[11]  Tamer Başar,et al.  H1-Optimal Control and Related Minimax Design Problems , 1995 .

[12]  Michael A. Gerber,et al.  EnergyPlus Energy Simulation Software , 2014 .

[13]  Peter B. Luh,et al.  Building Energy Management: Integrated Control of Active and Passive Heating, Cooling, Lighting, Shading, and Ventilation Systems , 2013, IEEE Transactions on Automation Science and Engineering.

[14]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

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

[16]  Charles Audet,et al.  Analysis of Generalized Pattern Searches , 2000, SIAM J. Optim..

[17]  Ethniko Metsovio Polytechneio.  European energy and transport : trends to 2030 , 2003 .

[18]  Jorge Nocedal,et al.  A trust region method based on interior point techniques for nonlinear programming , 2000, Math. Program..

[19]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[20]  Riccardo Scattolini,et al.  A stabilizing model-based predictive control algorithm for nonlinear systems , 2001, Autom..

[21]  Xiangjiang Zhou,et al.  Optimal operation of a large cooling system based on an empirical model , 2004 .

[22]  Rajesh Kumar,et al.  Energy analysis of a building using artificial neural network: A review , 2013 .

[23]  Rosemarie Velik,et al.  Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer , 2014 .