A latent data-mining-based method for verifying fuzzy addresses of electricity customers

Kunpeng Liu; Juncang Yang; Jing Yang

doi:10.1117/12.3037657

27 September 2024 A latent data-mining-based method for verifying fuzzy addresses of electricity customers

Kunpeng Liu, Juncang Yang, Jing Yang

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 1327518 (2024) https://doi.org/10.1117/12.3037657
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

The problem of ambiguity or incompleteness often exists in customer addresses, which affects the accuracy and efficiency of address matching. This method provides a systematic solution through the steps of data collection, preprocessing, feature extraction, model training and fuzzy address verification. First, the address data of electricity customers from power companies and preprocess them to standardize and correct errors. Second, features such as street names and door numbers are extracted from the address data. Then, the known accurate addresses are trained using suitable data mining algorithms to build models to identify and speculate the possible accurate locations of the fuzzy addresses. Finally, through fuzzy address verification, a list of most likely accurate addresses or candidate addresses is given based on the similarity of address features and the analytical ability of the model. The experimental results show that the method can improve the accuracy and efficiency of address verification for electricity customers and provide more reliable address matching service for power companies.

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

Citation Download Citation

Kunpeng Liu, Juncang Yang, and Jing Yang "A latent data-mining-based method for verifying fuzzy addresses of electricity customers", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 1327518 (27 September 2024); https://doi.org/10.1117/12.3037657

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