Neural Visualization for the Analysis of Energy and Water Consumptions in the Automotive Industry

Abstract

This study presents the application of neural models to a real-life problem in order to study the energy and water consumptions of an automotive multinational company for resources saving and environment protection. The aim is to visually and naturally analyse different consumptions data for a whole year, month by month, from factories and locations worldwide where different kinds of products are produced. The data are studied in order to see whether the geographical location, the month of the year or the technology used in each factory are relevant in terms of consumptions and then take actions for a greener production. The consumptions dataset is analysed using different neural projection models: Principal Component Analysis and Cooperative Maximum-Likelihood Hebbian Learning. This unsupervised dimensionality reduction techniques have been applied, and subsequent interesting conclusions are obtained.