Bio-inspired algorithms and artificial neural networks applied to smart load management systems to optimize energy usage
- Autores: Cristian Chiñas Palacios
- Directores de la Tesis: Carlos Vargas Salgado (dir. tes.), Elías Hurtado Pérez (dir. tes.)
- Lectura: En la Universitat Politècnica de València ( España ) en 2024
- Idioma: inglés
- Tribunal Calificador de la Tesis: Emilio Gómez Lázaro (presid.), Antonio Correcher Salvador (secret.), Sergio Valero Verdú (voc.)
- Programa de doctorado: Programa de Doctorado en Diseño, Fabricación y Gestión de Proyectos Industriales por la Universitat Politècnica de València
- Materias:
- Enlaces
- Tesis en acceso abierto en: RiuNet
- Resumen
Energy, communication, and computing are critical components of modern society, providing the foundation for technological development and economic growth. The close interrelation between these pillars has become increasingly apparent in recent years, as computing and data analysis advances have enabled new energy management and sustainability approaches. In this context, efficient energy usage has become a key focus for researchers, policymakers, and businesses alike. By harnessing the power of computing and machine learning (ML) techniques, it is possible to highlight the challenges of securing energy systems and optimizing energy usage, leading to the need for advanced techniques such as bio-inspired algorithms and neural networks.
This doctoral thesis aims to analyse load consumption and demand management programs and strategies in the current energy landscape. The central core presents an study on integrating bio-inspired algorithms, such as particle swarm optimization (PSO) and artificial neural networks (ANN) models in load management systems to meet load management challenges and use energy efficiently and securely.
The main body of this thesis comprises three scientific publications, each corresponding to a distinct stage within the overarching research framework of this study: the first stage covers the proposal of a low-cost architecture in energy systems introducing a cost-effective web-based SCADA system that was over 80% cheaper than a similar solution. The proposed low-cost architecture, tailored for microgrid testbeds, offers real-time monitoring, remote accessibility, and user-friendly control for academic and research applications. The second stage combined a cascade hybrid Particle Swarm Optimization (PSO) with feed-forward neural networks to accurately forecast and optimize energy demand in an AC microgrid, notably enhancing the integration of renewable energy sources like biomass gasification. The results showed that the proposed PSO-ANN model performs 23.2% better in terms of MSE than Feedforward Backpropagation (FF-BP) and Cascade forward propagation (CF-P) ANN models. The third and final stage focused on a smart load management system fortified with hybrid cryptography to ensure protected communication and data privacy, thereby effectively addressing energy security challenges in residential settings. Results showed that the proposed Security Residential System Load Management (SRS-LM) model was 37% better in performance (power cost, power utilization, computational time) and with a 60% peak load reduction compared to a Universal Smart Energy Meter (USEM) model.
