A satisfaction game for heating, ventilation and air conditioning control of smart buildings

Abstract

In this paper, the problem of distributed control of the heating, ventilation and air conditioning (HVAC) system in an energy-smart building is addressed. Using tools from game theory the interaction among several autonomous HVAC units is studied and simple learning dynamics based on trial-and-error learning are proposed to achieve equilibrium. In particular, it is shown that this algorithm reaches stochastically stable states that are equilibria and maximizers of the global welfare of the corresponding game. Simulation results demonstrate that dynamic distributed control for the HVAC system can significantly increase the energy efficiency of smart buildings.

Demand Response With Communicating Rational Consumers

“…Hence, if we are to consider another information sharing model that is not Bayesian and has a lower computational complexity, e.g. [28], we would expect our insights to be similar to the action-sharing model considered here as long as the proposed model aggregates information approximately correctly. As for the use of quadratic energy costs on the supply side, it is better to consider a model in which the cost for each device can be modeled as a linear function of the power dispatched from each device.…”

“…Proof: When N is large, we have the constant a Γ ≈ (N λ Γ σ+ 2α) −1 from Proposition 2 where λ Γ is as defined in Lemma 1. The comparisons follow from (28). Equation (29) shows that, with increasing σ, the demand variance grows as long as N λ Γ σ < 2α while it decreases with increasing σ when N λ Γ σ > 2α.…”

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

“…In addition to meeting the desired temperature, HVAC control systems are produced in order to sustain comfort within an enclosed space by producing a specific level of humidity, pressure, air motion, and air quality in an SB [40]. CO2, humidity and temperature levels in a building can affect occupant's health and comfort; consequently measuring CO2, humidity, and temperature in this context can improve personal wellbeing [41]. Heating and cooling systems consume a huge amount of energy in the buildings, so it is necessary to optimize it utilizing smart controllers and sensors in order to save operational costs.…”

Energy management and consumer modeling in smart grid systems

“…We assume that d i (•) is increasing and convex so as to model the increasing marginal dissatisfaction for the consumer as a function of deviation from the nominal consumption pattern, as in [86]. Here, we assume as in [87], is left for future work.…”