WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (WJIMT )
E-ISSN 2504-4766
P-ISSN 2682-5910
VOL. 9 NO. 6 2025
DOI: 10.56201/wjimt.v9.no6.2025.pg27.39
Ebimor Yinka Gbabo, Odira Kingsley Okenwa, Possible Emeka Chima
The increasing complexity and dynamic nature of regulatory frameworks in energy systems demand efficient, accurate, and timely compliance reporting. Traditional manual processes are no longer sufficient to meet these evolving requirements, necessitating the development of automated solutions. This paper presents a comprehensive framework for AI-enabled compliance automation designed to facilitate real-time regulatory reporting in energy infrastructures. By integrating machine learning and natural language processing, the framework interprets complex regulatory texts, processes heterogeneous operational data streams, and generates compliant reports with minimal latency. The proposed model architecture emphasizes robust data acquisition, preprocessing, scalable AI workflows, and real-time data processing mechanisms, ensuring adaptability to regulatory changes and operational demands. The findings demonstrate that AI-driven automation enhances reporting accuracy, reduces operational burdens, and strengthens transparency and accountability within energy markets. The study also identifies future research directions, including model interpretability, cybersecurity, and multi-jurisdictional compliance, underscoring the critical role of AI in advancing sustainable and resilient energy system governance.
Compliance Automation, Regulatory Reporting, Artificial Intelligence, Energy
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