Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Abstract

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

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

“…First, Table 2 shows in the third column the average daily energy consumed in both scenarios over the whole cooling season. As expected, the MPC approach allowed reducing the energy consumption by approximately 15-20% [10] (specifically, the average daily energy saving was 18.6%). This result is due to the inclusion of the energy saving criterion in the objectives of the cost function to be optimized over the receding horizon in Equation (15).…”

“…Only in the last two decades the more promising Model Predictive Control (MPC) approach is taking off: this control technique allows to effectively integrate issues such as disturbance rejection, constraint satisfaction, and slow-moving dynamic control together with energy efficiency strategies into the controller formulation. Furthermore, thanks to the decreasing costs of smart devices, the large availability of distributed sensors and data analytics tools, and in general the advances of Information and Communication Technology (ICT) [8,9], the implementation of optimal control approaches for the energy efficiency and thermal comfort optimization is becoming more immediate and affordable [10]. It is then evident that MPC becomes useless if it is not associated to a proper smart physical infrastructure that allows the collection/forwarding of actual data from/to the field [11].…”

Temperature control strategy for polymer electrolyte fuel cells

“…22,23 As an advanced process control algorithm, MPC has a wide variety of applications in battery management system optimization, electric vehicles control, energy storage arrangement, and microgrid power quality improvement. [24][25][26][27] Therefore, the aim of this research is, firstly, to develop a detailed thermal management model for a PEFC system. Secondly, on the basis of this model, a novel MPC strategy is designed to keep the temperature at the desired point, and it is compared with a conventional proportional integral derivative (PID) controller.…”

Learning-based Model Predictive Control for Smart Building Thermal Management

“…Having the weather and occupancy forecasts, the model predictive control (MPC) comes into play. MPC is a modern control technique that has been applied in many areas due to its ability to handle constrained control problems [3]. At each time instant, an optimal control problem is solved to obtain the optimal control action over the time horizon.…”