Predictive Temperature and Humidity Control in Integrated Building Energy Management Systems

Predictive Temperature and Humidity Control in Integrated Building Energy Management Systems

Diaz Guasgua, Andrea (TU Delft Mechanical, Maritime and Materials Engineering)

Keviczky, Tamas (mentor)

Delft University of Technology

Mechanical Engineering | Systems and Control

2019

Worldwide roughly 80% of heating and cooling systems in the building sector are currently dominated by fossil-fuel technologies. These systems, known as Heating, Ventilation and Air Conditioning (HVAC) systems have contributed to global climate change and global warming. These effects are the consequence of the increasing demand of HVAC systems over the last decades mainly due to population growth and improved building climate comfort. This has motivated the improvement of heating and cooling systems of buildings, for instance by implementing better control strategies. The control of HVAC systems aims to provide thermal comfort and acceptable indoor air quality. These factors are influenced by temperature and humidity. Thermal comfort and indoor air quality together with the reduction in energy consumption is the core of this work, we propose a nonlinear Model Predictive Control (MPC) as an approach to solve the temperature and humidity control while aiming to realize energy consumption reduction in buildings. MPC expresses the problem as an optimization program with constraints over a prediction horizon. The major advantage of using MPC in comparison to classic controllers for building climate comfort is the straightforward relation between temperature and humidity to energy consumption, which is quantified in the objective function definition. In this project, we represent the model of the building as a single zone space and we consider a cooling and heating coil to dehumidify and re-heat the air, respectively. These processes across the coils are graphically represented in the psychrometric chart, which allows us to define the overall system’s enthalpy as the difference between the mixed air and supply air conditions. This vector is integrated into the objective function in the MPC formulation. The MPC problem formulation is defined by the reference tracking of the temperature and humidity ratio in the zone and the energy minimization problem, which corresponds to a nonlinear constrained optimization problem. The constraints are described by the system dynamics of the zone, states constraints, and input constraints. The inputs correspond to the temperature and humidity ratio of the supply air. In this formulation, the state constraints and the reference tracking allow to indirectly limit the evolution of the relative humidity in the zone. The energy minimization problem corresponds to one of the following cases, the optimization with respect to the (i) air mass flow rate, (ii) the air mass flow rate and the sensible heat from ventilation and (iii) the air mass flow rate and the enthalpy vector. We investigate the opportunity to improve the energy efficiency by providing an analysis of the aforementioned scenarios. From which case (i) was taken as the reference controller. To solve the nonlinear program, we use the toolbox Yalmip and the Sequential Quadratic Programming (SQP) solver. The toolbox supplies the warm-starting for the optimization problem inside the bounds defined, and then it computes the solution. The results from these approaches confirm comfort requirements. And they also show that case (iii) provides the least energy consumption in comparison to (i) and (ii).

HVAC systems in buildings
Temperature and Humidity
Psychrometrics
nonlinear MPC control

http://resolver.tudelft.nl/uuid:36767636-7ed6-4b8d-96f1-88ceead9a23b

Student theses

master thesis

© 2019 Andrea Diaz Guasgua