Incorporating non-intrusive load monitoring into the residential electrical vehicle identification

Yu Song; Xinyi Zhang

doi:10.1117/12.3013296

29 November 2023 Incorporating non-intrusive load monitoring into the residential electrical vehicle identification

Yu Song, Xinyi Zhang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371D (2023) https://doi.org/10.1117/12.3013296
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

The extensive penetration of residential electric vehicles (EVs) has brought huge challenges to the operation of power grids. Recognizing the EV charging patterns and behaviors of residential EVs can help utilities operators make reasonable scheduling strategies for balancing power supply and consumption. In this paper, recent state-of-the-art deep learning models applied to load disaggregation are used to recognize charging patterns and behaviors of residential EVs. In addition, we propose a novel sequence-to-sequence model which is constructed by seven convolutional layers and extra two dense layers. And it can provide improved EV power consumption profiles for utility and users. Performance of proposed method has been validated using real data measured from several residential charging stations. Applicability of our proposed methodology has been confirmed by assessment results owing to its satisfactory precision. Besides, a comparative analysis has been made to demonstrate its effectiveness with regard to the disaggregation performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yu Song and Xinyi Zhang "Incorporating non-intrusive load monitoring into the residential electrical vehicle identification", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371D (29 November 2023); https://doi.org/10.1117/12.3013296

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

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Power consumption

Neural networks

Detection and tracking algorithms

Deep learning

RELATED CONTENT

An optimized hybrid evolutionary algorithm for accelerating automatic code optimization
Proceedings of SPIE (February 22 2023)

Multi-label pedestrian attribute recognition model based on domain generalization
Proceedings of SPIE (June 01 2023)

An infrared small vehicle target detection method based on deep...
Proceedings of SPIE (February 22 2023)

Lightweight Siamese network-based single-target tracking algorithm for pedestrians
Proceedings of SPIE (April 20 2023)

Ensemble MagNet:A fast training network against adversarial examples
Proceedings of SPIE (May 08 2023)

Signal classification based on self-attention mechanism in unlicensed bands
Proceedings of SPIE (October 19 2023)

Based on WOA-BP neural network power the behavior detection
Proceedings of SPIE (April 08 2024)

Subscribe to Digital Library

Receive Erratum Email Alert

Keywords/Phrases

Search In:

Publication Years