Artificial Intelligence Driven Optimization Management Algorithm for User Side Multi-source Data in Digital Electric Rooms

The swift technological advancement has led to the widespread adoption of intelligent collection devices in electrical power systems (EPS), ushering EPS into a new era of deep coupling within the “physics information society.” Consequently, harnessing this valuable information to craft precise user electricity behavior profiles and deliver personalized services has emerged as a focal research area in the power sector. To address this need, this paper introduces an innovative AI-powered optimization management algorithm tailored for user-side multi-source data in digital electrical rooms. This algorithm emphasizes the application of multi-source data fusion in EPS intelligent operation and maintenance, leveraging the strengths of deep learning (DL) technology. Its aim is to optimize and manage user-side multisource data, enhancing the accuracy of energy scheduling and fault warnings. Experimental findings demonstrate that the proposed algorithm not only accurately captures user electricity consumption patterns but also robustly supports EPS energy scheduling and fault detection, thereby paving the way for EPS intelligent operation and maintenance.