Artificial Intelligence for Energy Management: Investigate How Al and Machine Learning Algorithms Can Optimize Energy Consumption, Improve Efficiency, and Reduce Costs in Various Electrical Systems

Abstract

Artificial intelligence (AI) and machine learning offer tremendous potential to transform energy management across buildings, renewable sources, and electrical grid infrastructure. This paper provides a comprehensive review of relevant AI techniques and applications for optimizing energy usage, increasing efficiency, and reducing costs. Proposed AI methods include neural networks for predictive analytics, reinforcement learning for adaptive control systems, and computer vision for monitoring and fault detection. AI-based solutions for automated energy management range from smart thermostats in homes to AI agents coordinating decentralize distribution on smart grids. However, issues around explainability, security, and data quality must be addressed. If AI continues rapid advancement, further intelligent energy breakthroughs could emerge to cut consumption, maximize renewables, and bring the electrical grid into the 21st century.