Research in passive Heating, Ventilation, and Air Conditioning (HVAC) systems has gained traction over the last few years. Although passive HVAC is not a new concept, advances in environment sensing, control methods, and hardware have made it a more viable method. Some difficulties still exist, such as optimal sensor placement and optimal control strategies. Sensor selection is an important aspect of HVAC design. The system can become difficult to control with incorrect placement of sensors, resulting in higher energy consumption, lower comfort levels, or poor air quality.
There are essentially three methods to determine the optimal sensor location: model-driven, data-driven, and simulation-driven. The model-driven methods use mathematical models to maximize the observability of the system but are mostly used for simplified simulated rooms. Data-driven methods often use clustering algorithms, or maximize metrics such as entropy or mutual information. These methods focus on estimating the indoor air temperature distribution. Simulation-driven methods use simulations to determine the airflow or temperature
fields, often with CFD. These are used to find local hot spots or locations for fast detection of contaminants. No research was found that used sensor data of additional building components besides of the indoor air temperature.
In this work, the sensors are selected based on model prediction accuracy and the overall control performance to determine the effect of addition state measurements. A model is constructed to simulate the building, together with an MPC and an extended Kalman filter for state estimation. These are combined to run the optimization and determine the control performance. The sensor set average of each measured state is considered the true temperature. For all possible sensor combinations, the error of the combination average w.r.t. the true temperature is assumed Gaussian. The fitted Gaussian error distributions are then used as measurement noise in the model. The building and control response is simulated
with the measurement error over multiple days. Two algorithms are implemented to find the optimal sensor set: a predictive method and greedy method. The results are compared to each other and both methods showed that the indoor air temperature measurements have the largest effect on performance. Measuring additional states only resulted in a small increase in performance.

There is an urgent need for technical innovations in the construction industry to meet the European Parliament’s qualifications for reducing the energy footprint of buildings. This thesis is devoted to conducting research on control strategies that optimally manage passive energy sources, e.g., natural ventilation and solar irradiance, in combination with an active energy source. The main objective is to develop a control strategy that is both on-line applicable and maximizes the performance of the building energy management system in terms of passive fraction of energy while maintaining indoor thermal comfort. Energy-saving model predictive control (MPC) structures have been extensively researched in the literature on building energy management systems. These studies usually focus on the energy distribution in multi-zoned buildings and rarely consider optimal control of a single or multiple passive energy sources. In addition, most studied building models are based on general buildings and are established by means of simulation software tools. A more experimental study has not yet been conducted on optimal energy management systems for a building that is maximized in harvesting passive energy. This work investigates five MPC strategies as a way to optimize the operations of four solar blinds, a thermal chimney, and an active energy source. In these strategies, linear and nonlinear MPC are considered in the forms of centralized and hierarchical architectures. White-box modeling and linearization methods are adopted to develop the required linear and nonlinear building models. Thereafter, the proposed modeling methodology is validated by using experimental data. The hierarchical MPC architecture that considers a hybrid structure with a linear MPC agent for solar blind operations, a nonlinear MPC tracker for ventilation, a linear Kalman filter, and separated state-update loops appears to be the best-performing strategy. This control structure is also applied in a case study, in which it is tested on experimental data from the real-case office building, which is controlled by a rule-based control structure. The results show that the developed control structure is able to outperform the rule-based controller in terms of minimizing energy consumption and maintaining thermal comfort.