TRACE: Time series representation learning with contrastive embeddings for anomaly detection in photovoltaic systems

Reliable anomaly detection in photovoltaic (PV) inverters is critical for ensuring operational efficiency and reducing maintenance costs in renewable energy systems. We introduce TRACE (Time series Representation learning with Autoencoder-based Contrastive Embeddings), a self-supervised contrastive...

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Detalhes bibliográficos

Principais autores:	Chandana Priya Nivarthi, Zhixin Huang, Christian Gruhl, Bernhard Sick
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2026-01-01
coleção:	Energy and AI
Assuntos:	Time series anomaly detection Photovoltaic inverter monitoring Contrastive learning Representation learning Autoencoders Contrastive anomaly detection
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2666546825002022
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