Explicit MPC approach to PMV-based thermal comfort control

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

“…The first applications of MPC to the HVAC system control, see, e.g., [14,15], mainly consider the minimization of the energy consumption and the limitation of the temperature inside a constrained range. Evidently, bounding and controlling only the temperature does not imply the maximal comfort of occupiers [16]. Therefore, the Predicted Mean Vote (PMV) index (standardized by ISO [17]) has been introduced to comprehensively express the thermal comfort conditions [7,18] and has been subsequently combined with the MPC technique with the aim of optimizing the indoor thermal comfort in smart building environments.…”

“…However, the non-linear nature of the PMV index represents a significant challenge in the design and implementation of such control frameworks. In effect, the MPC controller is required to solve, at each sampling time, a non-linear optimization problem that evidently requires a large amount of computational resources and can lead to long computation times [16]. Furthermore, the non linearity of the PMV limits the applicability and scalability of the control problem formulation.…”

“…Similarly, Alamin et al [21] propose an economic MPC approach to control the thermal comfort of a bioclimatic building room using a first order linear time-invariant system for modelling the relation between the PMV index and the speed of fan coil units. In [16] the authors formulate the MPC problem as a parametric quadratic program and include a linearized version of the PMV index in the constraint set of the problem. Furthermore, with the aim of simplifying the resolution of the optimization problem, authors reformulate the MPC optimization problem in an explicit form.…”

“…It has to be highlighted that all the cited contributions consider an implicit MPC formulation where the control law is defined by solving the optimization problem in real-time, however in most of the cited works [16,20,[22][23][24], all the proposed algorithms are tested in a simulation environment only, thus without providing information on the actual control architecture implementation. This paper overcomes this important limitation.…”

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

“…For example, the solar radiation effect on comfort has been studied in [24] and the references within. Industry and research communities express the need for appropriate thermal comfort definitions for the purpose of building control [25,26,27]. To date, the most common thermal comfort index adopted by international standards is Fanger's Predictive Mean Vote (PMV) model [28]: ISO 7730 [23], and the adaptive standards EN 15251 [29] and ASHRAE 55 [30] rely on it.…”