Parabolic Trough Solar Collectors (PTCs) are a prevalent technology used to provide heat in industrial processes. The primary objective of the control system is to regulate the Heat Transfer Fluid (HTF) temperature to a target value, despite external disturbances. In this study, a Reinforcement Learning (RL) based control system is developed to ensure accurate tracking of the temperature reference at the PTC output, considering the disturbances introduced by solar irradiance. Furthermore, the performance of the proposed system is compared with classical control strategies such as Proportional-Integral-Derivative (PID) and feedforward control, aiming to enhance disturbance rejection and overall performance. The results demonstrate the potential of RL-based control systems for managing complex and non-linear systems.

In this study, a digital twin for a flat plate solar collector field is proposed. This kind of system is used to reduce carbon dioxide emissions in bioclimatic buildings to convert them into Zero Energy Buildings. The core of the digital twin is an Artificial Neural Network prediction model, which is a good alternative to models based on physical equations for modeling systems with strong non-linearities, such as the ones found in flat plate solar collectors. The Artificial Neural Network prediction model is calibrated and validated with data saved during one year of operation comprising sunny days, cloudy days, partially cloudy days and non-operation days. Validation shows good results using several statistical metrics, suggesting that the Artificial Neural Network model is suitable for operation and control purposes. With a highly accurate virtual representation, the Artificial Neural Network model allows data analysis of the plant operator, prediction of behavior, and offers recommendations for optimizing system performance. In addition, the digital twin presented as part of this work is not just limited to the model, but is also enriched by the integration of data acquisition technologies and a user interface into a web page. This innovative integration establishes a robust framework for proactive, real-time decision-making and efficient management of the plant, ensuring enhanced system operation and sustainability.

In this work, we introduce an energy management system (EMS) that focuses on minimizing two objective functions simultaneously, i.e. the cost of energy and the production of CO2in a microgrid. As a result, a set of equally valuable solutions are proposed. The decision-maker is then in charge of deciding and considering the solution that best fits his/her current preferences. If the condition changes, the user or operator can select another solution without solving the optimization problem again. To test the EMS, the microgrid located at the CIESOL building at the University of Almería has been considered.

This paper deals with modeling the temperature of a room in a smart building. Two different approaches are analyzed and compared. The first one is based on a black-box identification procedure, which yields a fairly simple dynamical system that is suitable for real-time control. The second approach is based on first principles, it requires a complex and time consuming calibration procedure but it is capable of describing accurately the physical behavior of the system. The trade-off between the accuracy of the model and its computational complexity is evaluated by using experimental data collected from a smart pilot building located in Almería, Spain.

The interest in model based current control has been steadily growing during the last years. In particular, predictive techniques using a model and an exhaustive optimizer have been used, with many variations, for current control of IM using a VSI. In this paper it is shown that predictive controllers using the standard quadratic cost function seem to be prone to produce currents with subharmonic and non-integer harmonic content. This subject is rarely encountered in the predictive literature. The study shows that the harmonic content these controllers depend heavily on the precision of the predictive model. The relationship between model accuracy and harmonic distribution is illustrated using simulations of a three-phase induction machine. The results unveil complex relationships among factors that are analyzed providing guidelines that help in the practical design of predictive controllers.

Comfort conditions inside buildings are a problem that is being widely analyzed, since it has a direct effect on users' productivity, and an indirect effect on energy saving. Hence, in order to maintain thermal comfort and indoor-air quality inside a certain environment, it is required to perform a proper management of its active and passive components, as the HVAC (Heating, Ventilation and Air Conditioning) systems, and natural ventilation through windows. This paper presents a multivariable nonlinear model predictive control system to simultaneously maintain thermal comfort and indoor-air quality by means of forced and natural ventilation. In order to probe the effectiveness of the proposed control approach, simulation results obtained in a characteristic room of a bioclimatic building are included and widely commented.

Industrial processes are characterized by changing dynamics associated to different operating points. Control systems usually face this behavior by adapting the control parameters or switching among controllers, in order to achieve high performance of the system in the whole process operation range. Different methods have been developed to avoid undesirable transients in the closed loop output signal when changes in the control system structure occur. This paper presents a comparative study of some of these methods showing simulation results.