Self-supervised learning method for consumer-level behind-the-meter PV estimation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Scopus Citations
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Detail(s)

Original languageEnglish
Article number119961
Journal / PublicationApplied Energy
Volume326
Online published28 Sept 2022
Publication statusPublished - 15 Nov 2022

Abstract

Driven by cost reduction and sustainable policies, the penetration of distributed photovoltaic (PV) systems has deepened in recent years. Most of these PV systems are installed behind the meter (BTM), where utilities cannot monitor their output levels directly. Some supervised methods have been studied to estimate BTM PV generation. These methods, however, cannot achieve accurate estimation without the dependency on training data labeled by additional measurements. As an alternative, a self-supervised learning method is proposed in this paper to train supervised estimation models from unlabeled data. Specifically, our proposed method synthesizes pseudo labels for unlabeled net load measurements using PV generation measurements of a small group of PV sites. Moreover, an end-to-end network architecture is proposed as the base estimation model. Based on a linear embedding of PV generation, the proposed end-to-end architecture can be directly trained with PV generation labels, which leads to a simplified training process and improved estimation performance. Extensive numerical simulations on two datasets from different hemispheres are carried out to verify the effectiveness of the proposed methodology.

Research Area(s)

  • Behind-the-meter, Distributed photovoltaic, Net load disaggregation, Self-supervised learning, Smart meter

Citation Format(s)

Self-supervised learning method for consumer-level behind-the-meter PV estimation. / Liu, Chao Charles; Chen, Hongkun; Shi, Jing et al.
In: Applied Energy, Vol. 326, 119961, 15.11.2022.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review