Deep learning-based power outage impact on users and power outage loss analysis

Chen Lin

doi:10.1117/12.3049232

17 October 2024 Deep learning-based power outage impact on users and power outage loss analysis

Chen Lin

Author Affiliations +

Proceedings Volume 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024); 132890Q (2024) https://doi.org/10.1117/12.3049232
Event: The International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 2024, Hangzhou, China

Abstract

This paper proposes a method for analyzing the impact of power outages on users and predicting losses based on deep learning. By processing heterogeneous data from multiple sources, a high-quality dataset is constructed, and algorithms such as MLP, Random Forest, and SVR are used to train models for user classification and loss prediction. Based on this, a comprehensive analysis system integrating data management, model analysis, and visualization display is designed and implemented. Through testing, the system's usability, stability, and security are verified. This research provides a datadriven analysis tool for power companies and government decision-making departments, with important theoretical and practical significance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chen Lin "Deep learning-based power outage impact on users and power outage loss analysis", Proc. SPIE 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 132890Q (17 October 2024); https://doi.org/10.1117/12.3049232

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