Arquitetura de modelos híbridos, machine learning e otimizadores para análise de consumo de energia elétrica e produtividade em pintura automotiva

Abstract

Strategies for optimizing energy consumption in the painting stages are emerging as key factors in promoting more sustainable and competitive production in the automotive sector. This dissertation seeks to predict energy consumption and maximize productivity in automotive painting, using an approach that combines variable selection, hybrid models, hyperparameters of these models and meta-heuristic optimization in a 3-stage architecture. Automotive painting processes have variables in the form of time series that describe the history of energy consumption. In stage 1, the best machine learning model is chosen (Random Forest, Long-Short Term Memory, XGBoost and GRU-LSTM) to predict energy consumption time series at t+1. In step 2, the RF, XGBoost and Dense Artificial Neural Network (ANN) models are evaluated to select the best predictor of the number of vehicles produced (cycles). In step 3, the best metaheuristic between Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is selected to optimize the energy consumption predicted by the best model from step 1, using the best model from step 2 as a fitness measure. The final architecture reduced the energy consumed by up to 16% and increased the cycle by 127%, using the GRU-LSTM models in step 1, Dense ANN in step 2 and DE in step 3. The results highlight the opportunity to use the proposed approach to optimize energy consumption and productivity in automotive painting.