Revolutionizing Consumer Power Management: Unveiling Power Grid Feasibility Analysis Using Machine Learning

As distributed energy sources become more prevalent, maintaining power grid stability is increasingly challenging. By integrating machine intelligence and communication technologies, traditional power networks could transform into smart grids. Through machine learning and artificial intelligence, these smart grids can adeptly respond to unexpected changes in consumer demand, power interruptions, surges in renewable energy production, and other crucial situations. Variations in power generation and loads, along with changes in the power system’s structure, lead to varying shifts in the entire network’s active power. Detecting these fluctuations through manual analysis is laborious, but machine learning can be highly impactful in this regard. This study aims to collect comprehensive data for analyzing grid stability, utilizing machine learning tools to thoroughly examine the data, and adopting a multimodal approach to compare model outcomes for an improved solution strategy. Our analysis reveals an accuracy exceeding 0.97, indicating strong potential for practical application and implications.