Incentive-Based Mechanism for Truthful Occupant Comfort Feedback in Human-in-the-Loop Building Thermal Management

Energy inefficiency and underlying occupant discomfort associated with building thermal management systems have led to the development of a human-in-the-loop control system. Minimizing total energy use and maximizing comfort of all the occupants present is the major objective of such system based on human comfort feedback. However, the approaches proposed so far lack a built-in mechanism to elicit truthful comfort feedback from the occupants. In this paper, we utilize an incentive-based mechanism design framework to elicit true thermal comfort feedback (function) from the occupants present. This requires the building occupants to “purchase” or spend credits to achieve their personalized comfort levels within their zone of occupancy. The comfort pricing policy has been derived as an extension of the Vickrey–Clarke–Groves pricing. It ensures incentive compatibility of the mechanism, which implies that an occupant acting in self-interest cannot stand to benefit by declaring their comfort function untruthfully. This would hold irrespective of the thermal comfort choices made by the other occupants present in the building. We further propose as to how this mechanism could be implemented in practice with limited comfort feedback complexity, where the building operator would iteratively learn and refine the occupants comfort function based on simple 2-D comfort feedback (preferred temperature setting, and willingness-to-pay value) by the occupant. Simulations using parameters based on our Watervliet experimental facility demonstrates the effectiveness of the proposed mechanism.

[1]  Suman Banerjee,et al.  Hot, cold and in between: enabling fine-grained environmental control in homes for efficiency and comfort , 2014, e-Energy.

[2]  Koushik Kar,et al.  Extended second price auctions for Plug-in Electric Vehicle (PEV) charging in smart distribution grids , 2014, 2014 American Control Conference.

[3]  M. Hancock,et al.  Do people like to feel ‘neutral’?: Exploring the variation of the desired thermal sensation on the ASHRAE scale , 2007 .

[4]  D. Kolokotsa,et al.  Reinforcement learning for energy conservation and comfort in buildings , 2007 .

[5]  Michael A. Humphreys,et al.  Field Studies of Indoor Thermal Comfort and the Progress of the Adaptive Approach , 2007 .

[6]  Gregor P. Henze Energy and Cost Minimal Control of Active and Passive Building Thermal Storage Inventory , 2005 .

[7]  Branislav Kusy,et al.  Model-free HVAC control using occupant feedback , 2013, 38th Annual IEEE Conference on Local Computer Networks - Workshops.

[8]  Kevin L. Moore,et al.  Dynamic Consensus Networks with Application to the Analysis of Building Thermal Processes , 2011 .

[9]  Koushik Kar,et al.  Extended Second Price Auctions With Elastic Supply for PEV Charging in the Smart Grid , 2016, IEEE Transactions on Smart Grid.

[10]  Koushik Kar,et al.  Building Temperature Control With Active Occupant Feedback , 2014 .

[11]  Koushik Kar,et al.  Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback , 2018 .

[12]  Francesco Borrelli,et al.  Analysis of local optima in predictive control for energy efficient buildings , 2013 .

[13]  Francesco Borrelli,et al.  Fast stochastic predictive control for building temperature regulation , 2012, 2012 American Control Conference (ACC).

[14]  John T. Wen,et al.  Design and instrumentation of an intelligent building testbed , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[15]  James E. Braun,et al.  Reducing energy costs and peak electrical demand through optimal control of building thermal storage , 1990 .

[16]  J A Stolwijk,et al.  MATHEMATICAL MODELS OF THERMAL REGULATION , 1980, Annals of the New York Academy of Sciences.

[17]  V. Geros,et al.  Implementation of an integrated indoor environment and energy management system , 2005 .

[18]  D. A. McIntyre,et al.  Chamber studies—reductio ad absurdum? , 1982 .

[19]  Koushik Kar,et al.  Collaborative Energy and Thermal Comfort Management Through Distributed Consensus Algorithms , 2015, IEEE Transactions on Automation Science and Engineering.

[20]  Sami Karjalainen,et al.  User problems with individual temperature control in offices , 2007 .

[21]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[22]  Christian Ghiaus,et al.  Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling , 2012 .

[23]  Lukas Ferkl,et al.  Model predictive control of a building heating system: The first experience , 2011 .

[24]  Gilles Fraisse,et al.  Development of a simplified and accurate building model based on electrical analogy , 2002 .

[25]  Gail Brager,et al.  Developing an adaptive model of thermal comfort and preference , 1998 .

[26]  Koushik Kar,et al.  BEES: Real-time occupant feedback and environmental learning framework for collaborative thermal management in multi-zone, multi-occupant buildings , 2016 .

[27]  Yi Jiang,et al.  Preliminary study of learning individual thermal complaint behavior using one-class classifier for indoor environment control , 2014 .

[28]  Jie Zhao,et al.  Occupant-oriented mixed-mode EnergyPlus predictive control simulation , 2016 .

[29]  MengChu Zhou,et al.  Technologies toward thermal comfort-based and energy-efficient HVAC systems: A review , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[30]  van J Joost Hoof,et al.  Forty years of Fanger’s model of thermal comfort: comfort for all? , 2008 .

[31]  Hui Zhang,et al.  Thermal sensation and comfort models for non-uniform and transient environments: Part III: whole-body sensation and comfort , 2009 .

[32]  Gregor P. Henze,et al.  Evaluation of optimal control for active and passive building thermal storage , 2004 .

[33]  Georgios B. Giannakis,et al.  Residential demand response with interruptible tasks: Duality and algorithms , 2011, IEEE Conference on Decision and Control and European Control Conference.

[34]  John T. Wen,et al.  Building temperature control: A passivity-based approach , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[35]  Yi Jiang,et al.  Experimental study of group thermal comfort model , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[36]  S.A. Bortoff,et al.  Building HVAC control systems - role of controls and optimization , 2006, 2006 American Control Conference.

[37]  A. P. Gagge,et al.  An Effective Temperature Scale Based on a Simple Model of Human Physiological Regulatiry Response , 1972 .

[38]  Alberto Cerpa,et al.  Thermovote: participatory sensing for efficient building HVAC conditioning , 2012, BuildSys@SenSys.

[39]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[40]  Manfred Morari,et al.  BRCM Matlab Toolbox: Model generation for model predictive building control , 2014, 2014 American Control Conference.