Artificial Neural Networks for Energy Demand Prediction in an Economic MPC‐Based Energy Management System

Abstract

Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role inimplementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologiesfor 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 theenergy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop aframework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to providefeedforward power demand predictions.Next, we present an economicMPCformulation that includes the prediction model for thedisturbance within the optimization problem to be solved by the MPC strategy.We carried out simulations with different scenariosof energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performanceof the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying atall times with the objectives and operational restrictions imposed on the system.