Keywords
active service recommendation, electricity complaint, potential service needs, LSTM-Attention
Abstract
In order to improve the level of electricity consumption, it is an inevitable trend to use artificial intelligence technology to provide active service to electricity customers. Under the background, an active customer service recommendation method is proposed based on LSTM-Attention fusion considering the lack of research on active customer service in the power industry. The proposed method can effectively solve the problems of gradient mass and gradient explosion in the service recommendation of a single deep learning model. Firstly, a model is established for extracting potential service demands of customers from electric power complaint work orders. Then, an active service recommendation method is obtained for electric power customers based on the LSTM-Attention fusion algorithm. Finally, an electric power customer complaint work order in one city is included to verify the algorithm and model. It is shown that this method is effective.
DOI
10.19781/j.issn.1673-9140.2022.02.025
First Page
213
Last Page
218
Recommended Citation
ZHANG, Di; WANG, Tao; ZHU, Jiran; TANG, Haiguo; ZHANG, Zhidan; TANG, Xiaowei; and YAN, Hongwen
(2022)
"Active service recommendation method for power customers based on LSTM-Attention fusion,"
Journal of Electric Power Science and Technology: Vol. 37:
Iss.
2, Article 25.
DOI: 10.19781/j.issn.1673-9140.2022.02.025
Available at:
https://jepst.researchcommons.org/journal/vol37/iss2/25