A Smart Energy System Design Using Neuro-Inspired Models and Evolutionary Optimization

Abstract

Brain-inspired artificial intelligence offers a potential avenue for enhancing adaptive, efficient and intelligent computing systems in various applications. This research seeks to investigate the structure, efficiency and implementation of neuro-inspired algorithms and models for improving machine learning in health care, edge computing and smart systems. This study adopts a holistic approach to integrating recent research on brain-inspired algorithms, such as evolutionary algorithms, synaptic learning rules, and hybrid deep learning models, and compares their efficiency, adaptability and interpretability. The findings show that neuro-inspired models offer substantial enhancements in learning efficiency, energy-efficiency, and decision-making, particularly in complex and big data environments like medical applications and IoT systems. Moreover, incorporating hardware technologies such as memristor chips and AI accelerators also improves speed and scalability. The results also demonstrate the ability of these models to enable continuous learning and dynamic adaptation. In summary, neuro-inspired AI offers a novel avenue to build next-generation energy-efficient and adaptive intelligent systems, but there are also challenges in scaling and practical implementation that need to be addressed.