Mejora de la eficiencia energetica de ciudades inteligentes aplicando tecnicas de soft computing

Abstract

This Dissertation is a research with the aim to improve the energy efficiency with soft computing techniques applied on highly consuming systems deployed in cities. ‘Smart city’, ‘Smart building’ and ‘Smart street lighting’ are convenient concepts for the energy efficiency improvement and sustainability research, by using the Information and Communications Technologies (ICT), procuring better living conditions for citizens. This study focuses on building’s Heating, Ventilation and Air Conditioning (HVAC) systems and public street lighting, providing artificial intelligence to systems’ management and robustness to control. This work is made of three scientific articles written by the Author, published in international journals with high impact indexation proving the research interest of this Dissertation. They address energy efficiency problems in cities with autonomic management, fuzzy logic control systems and artificial neural networks models, all considered under the Artificial Intelligence’ soft computing techniques. The achieved results are of noticeable scientific and technical interest. Thus, the first article describes the new autonomic management architecture of building’s HVAC system with Autonomous Cycle of Data Analysis Tasks (ACODAT) and proves its suitability. The second article proposes an advanced fuzzy control method: Learning Algorithm for Multivariate Data Analysis (LAMDA), that improves the response to uncertainty and context changes of nonlinear HVAC systems. The third article analyzes the performance of different MLP’s architectures to work as the fitness function for fast street lighting simulations with multiple optimization objectives.