As one of the main consumers of primary energy globally, buildings have been among the main targets for implementing energy efficiency solutions, such as building control strategies that maintain occupant comfort and reduce operating costs. The design of such control schemes relies on a thermal model of the building to predict indoor temperature. The model should be sufficiently accurate to describe building dynamics but simple enough to remain optimal for control purposes. This paper proposes a methodology to identify thermal RC networks to model building thermal dynamics of a residential buildings located in humid and rainy climates, a topic not widely covered in current literature. The candidate models for the methodology are determined through a parameter dispersion study, which consists of training the models multiple times and checking if the parameters converge to a single value regardless of their initial value. Then the effect of the training dataset characteristics on model performance is studied. The methodology is established and then tested in a residential case study in Panama from these conclusions. Results show that a linear model with few parameters and trained with only 10 days of data can successfully represent a system with prominent nonlinear phenomena. The model with the best performance during active operation has a validation root mean square error of 0.36°C, which is satisfactory for controller design purposes. The model is then used to tune a proportional integral derivative controller, successfully employed to maintain the desired indoor temperature. Using the identified model to calibrate the controller avoids tedious trial and error procedures.

Two models were developed to simulate energy flows in a mangrove area of A. germinans and A. bicolor in the Bay of Panama, considering the importance of these areas in CO₂ fixation. The first model (black box) consisted of the use of artificial neural networks for estimation, using meteorological data and energy flows calculated by the Eddy Covariance method for model training. The second model (grey box) used the RC circuit theory, considering a non-steady state model for the flow of water from the ground to the atmosphere. A methodology was developed to reduce the uncertainty of the data collected by the sensors in the field. The black box model managed to predict the fluxes of latent heat (R² > 0.91), sensible heat (R² > 0.86), CO₂ (R² > 0.88), and the potential of water in the air (R² > 0.88) satisfactorily, while the grey box model generated R² values of 0.43 and 0.37, indicating that it requires further analysis regarding the structuring of the equations and parameters used. The application of the methodology to filter the data improved the effectiveness of the model during the predictions, reducing the computational capacity necessary for the resolution of the iterations.