Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
Date
2024-10-20
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International Journal of Robust and Nonlinear Control
Abstract
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.
Description
Keywords
artificial neural networks, deep learning, disturbance prediction, economic model predictive control, long short-term memory, microgrid
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