Montanaro, Umberto (2016) Building Indoor Air Temperature and Humidity Control via Innovative Techniques. [Tesi di dottorato]

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In this thesis a novel Model Reference Adaptive Control approach, developed to tame the thermohygrometric behaviour of buildings and to guarantee the indoor comfort, is presented. The main advantages of the proposed method are: i) robustness with respect to a large class of perturbations, external disturbances, nonlinear unmodelled dynamics or parameters uncertainty; ii) ability to impose some given profiles to the thermohygrometric variables, iii) accurate regulation and fast tracking of indoor air temperature and humidity in the case of stringent requirements in special building spaces. In order to analyse the effectiveness and robustness of the proposed control strategy, several case studies have been carried out. They refer to some reference buildings with different geometry, use and construction materials (also including phase change materials integrated into the building envelope) simulated in different weather conditions. In addition, the control of multi-zone thermal systems have been also considered in the relevant and innovative case of thermal zones completely included in others (e.g., an expo indoor space of a museum building including a display/case, special indoor hospitals spaces including multiple infant-incubators, etc.). Finally, model based control solutions have been designed for the control of the thermohygrometric dynamics in order to better point out the robustness of the adaptive solution to the computation of the required sensible loads. Results confirm the ability of the developed approach to achieve the selected indoor air temperature and humidity conditions in order to guarantee indoor comfort in uncertain conditions. In order to devise model based controllers, the design of a procedure for generating low order building models from detailed ones is proposed. The mismatch between the outputs of the simplified models with respect to those of the detailed ones have been measured quantitatively via a set of performance indexes. Results confirm that reduced order models of buildings can effectively predict i) indoor air temperature, ii) energy consumptions, iii) sensible heat demands, as well as iv) comfort of occupants (e.g., predicted mean vote, predicted percentage dissatisfied, and mean radiant temperature). Hence, they can be used not only for the design of advanced model based controllers, but also for i) reducing drastically the computation time to get an insight into building energy performance, especially when a large set of simulations are required, ii) deriving mathematical models of building dynamics via reverse engineering methods, when experimental data are available and iii) simulate cluster of buildings.

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