Demand-side genus matching based on GAF and generalised self-attention mechanism

Hanjun Deng; Minqi Yu; Xinyao Lu

doi:10.1117/12.3037496

27 September 2024 Demand-side genus matching based on GAF and generalised self-attention mechanism

Hanjun Deng, Minqi Yu, Xinyao Lu

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132751C (2024) https://doi.org/10.1117/12.3037496
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

The study of matching power consumption attributes of station users plays an important role in smart grids, providing an important basis for tasks such as power dispatch and energy optimisation. Traditional classification tasks usually use hand-designed feature extractors to classify the users in the station area. To solve the problem that its performance is limited by the feature expression, the daily power data of the users are transformed into the form of two-dimensional images by introducing the Gramian Angular Field (GAF) in order to capture their time-varying characteristics, and the image features are learnt by combining with the generalized self-attention mechanism, and the network model based on Transformer is used for the analysis of the generated feature images to achieve a more accurate classification. The generated feature images are analysed to achieve more accurate user category classification. The simulation experiments show that the provided model and method have a good effect of station classification.

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

Citation Download Citation

Hanjun Deng, Minqi Yu, and Xinyao Lu "Demand-side genus matching based on GAF and generalised self-attention mechanism", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132751C (27 September 2024); https://doi.org/10.1117/12.3037496

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE