Using a Hybrid Model of Machine Learning and Meta-heuristic Optimization to Predict Electricity Consumption and Productivity in Automotive Painting

Abstract

This article seeks to predict energy consumption (EC) 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-step architecture. Automotive painting processes have variables in the form of time series that describe the history of the EC. In step 1, the best machine learning model is chosen (RF, LSTM, XGBoost and GRU-LSTM) to predict EC time series at t+1. In step 2, the RF, XGBoost and Dense ANN models are evaluated to select the best predictor of the number of vehicles produced (cycles). In step 3, the best metaheuristic between GA, DE and PSO is selected to optimize the EC predicted by the best model from step 1, using the best model from step 2 as a fitness measure. The final architecture reduced the EC 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 EC and productivity in automotive painting.