FRRQ - Producción de Investigación

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    Approximating the solution of an economic MPC using artificial neural networks
    (IEEE Xplore, 2024-05-21) Alarcón, Rodrigo Germán; Alarcón, Martín Alejandro; González, Alejandro H.; Ferramosca, Antonio
    Abstract: Economic model predictive control is a recognized advanced control strategy which calculates control actions by solving an optimization problem in real time. The issue of numerical computation is the main barrier to implementing this type of controller. Deep learning has emerged as a promising solution to reduce the computational cost. This paper proposes a deep learning approximation of an Economic MPC, particularly with artificial neural networks, of the control strategy for managing energy resources in a residential microgrid. Operational data were generated from the solution established by the controller to train, validate and test the neural network using Matlab. Simulation results showed that the proposed approach can approximate the control strategy correctly.
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    Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
    (International Journal of Robust and Nonlinear Control, 2024-10-20) Alarcón, Rodrigo Germán; Alarcón, Martín Alejandro; González, Alejandro H.; Ferramosca, Antonio
    ABSTRACT Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop a framework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.