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Dr Yinyan Liu is a Postdoctoral Research Associate at the School of Photovoltaic and Renewable Energy Engineering at the University of New South Wales. Her research interests include modelling and optimisation of integrated energy systems, fault diagnosis of PV systems, energy management for decarbonisation and decentralisation, and applications of machine learning technologies for smart grids and smart cities.
She was awarded the Rising Star Women in Engineering award, the Faculty of Engineering PhD Completion Award, a full scholarship for the API’s Powerful Women Leadership Program in 2023, and the University of Sydney International Scholarship. Her recent publications include the 2025 paper 'A methodological review of cost-effective data-driven fault detection and diagnosis in distributed photovoltaic systems' in Applied Energy, the 2025 paper 'Economic feasibility and backup capabilities of solar-battery systems for residential customers' in Energy, the 2025 paper 'Techno-economic optimization of electric water heater and battery energy storage for residential dwellings with rooftop PV systems' in Energy and Buildings, the 2022 paper 'A home energy management system incorporating data-driven uncertainty-aware user preference' in Applied Energy, and the 2022 paper 'SAMNet: Toward Latency-Free Non-Intrusive Load Monitoring via Multi-Task Deep Learning' in IEEE Transactions on Smart Grid.
Discover how UTS and UNSW researchers developed a rule-based method for detecting PV system underperformance using only inverter data, validated on 1000+ Australian systems.
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