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

• Published in: Applied energy Vol. 326; p. 119961
• Main Authors: Liu, Chao Charles, Chen, Hongkun, Shi, Jing, Chen, Lei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2022
• Subjects: Behind-the-meter; Distributed photovoltaic; Net load disaggregation; Self-supervised learning; Smart meter; Behind-the-meter; Distributed photovoltaic; Net load disaggregation; Smart meter; Self-supervised learning
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2022.119961

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. •The self-supervised method trains estimation models from unlabeled data.•The end-to-end network architecture outperforms models based on physical models.•The significance of self-supervised learning is analyzed via external validation.•Robust training ensures good performance with high contamination ratios.