AI-Enhanced Decentralized Energy Market Prediction forAutonomous Microgrids

This scholarly article investigates the amalgamation of Natural Language Processing (NLP)and machine learning techniques to optimize safety management through the automation ofsafety occurrence report analyses. By leveraging advancements in deep learningmethodologies, particularly Bidirectional Transformers, the research demonstrates enhancedprecision and diminished requirements for text preprocessing. A case study approach isemployed to derive valuable insights from discrete safety incidents, thereby facilitatinginformed decision-making and the continuous enhancement of safety protocols. Principalareas of emphasis encompass the automation of safety incident identification andcategorization to enable real-time analysis and proactive risk mitigation, as well as theutilization of predictive modeling to anticipate future safety challenges grounded in historicaldata patterns. The manuscript also delineates the incorporation of energy-efficientmethodologies, particularly within the framework of the Internet of Things (IoT), as a crucialavenue for forthcoming research endeavors. Underpinned by comprehensive citations, thefindings elucidate a technological convergence that possesses the potential to revolutionizerisk management, thereby nurturing a culture of ongoing improvement and resilience withinorganizations.

کلیدواژه ها:

Natural Language Processing ، Machine Learning ، Safety Management ، Bidirectional Transformers ، Energy Efficiency