ORCID as entered in ROS
https://orcid.org/0000-0002-3345-5912
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Conference Papers
, 2025, 'Fully automated detection of multi-type solar cell defects and severity using label-free learning', Bangkok, Thailand, presented at
, 2025, 'A clustering-based approach to accurate performance loss rate estimation of solar farms', Bangkok, Thailand, presented at
, 2025, 'A unified framework for automated diagnostics of solar modules using multi-modal images', Bangkok, Thailand, presented at
, 2025, 'An automated diagnostics algorithm for performance assessment of utility-scale solar farms', Bangkok, Thailand, presented at
, 2025, 'Developing an open-access dataset of solar cell luminescence images using generative modelling', Bangkok, Thailand, presented at
, 2025, 'Optimal rainfall threshold for natural cleaning of dirt on panels', Cairns, Australia, presented at
, 2025, 'Automated analysis of performance losses in solar cells using explainable artificial intelligence', Montreal, Quebec, presented at
, 2025, 'Enhancing solar farm performance monitoring using attention-based LSTM models and satellite weather data', Montreal, Quebec, presented at
, 2025, 'Evaluating statistical degradation rate calculation methods using synthetic datasets and current-voltage measurements', Montreal, Quebec, presented at
, 2025, 'Machine learning meets PVsyst: A novel framework to detect, classify, and forecast faults in utility-scale PV', Montreal, Quebec, presented at
, 2025, 'Minimising the uncertainty in performance loss rate calculation for photovoltaic systems: A clustering-based year-on-year approach', Montreal, Quebec, presented at
, 2025, 'Probabilistic soiling estimates for photovoltaic performance', Padova, Italy, presented at
, 2024, 'An unsupervised learning approach to model potential induced degradation in photovoltaic modules', in Abdullah-Vetter Z (ed.), Sydney, Australia, presented at
, 2024, 'Applications of a machine learning model for real-time interpolated PVsyst simulations', in Applications of a machine learning model for real-time interpolated PVsyst simulations, Sydney, Australia, presented at
, 2024, 'Assessing the practical accuracy and limitations of PVsyst models for performance assessment of utility photovoltaic systems', Sydney, Australia, presented at
, 2024, 'Automated multiclass and severity classification of defects in photovoltaic module luminescence images using unsupervised machine learning', Sydney, Australia, presented at
, 2024, 'Development and application of virtual photovoltaic soiling sensors', Sydney, Australia, presented at
, 2024, 'Efficiency loss analysis in solar cells using explainable AI on I-V data and luminescence images', Sydney, Australia, presented at
, 2024, 'Evaluating statistical degradation rate calculation methods using current-voltage measurements', Sydney, Australia, presented at
, 2024, 'Optimising lifecycle management for utility-scale photovoltaic systems: An integrated electrical, economic, and environmental strategy', Sydney, Australia, presented at
, 2024, 'Regional driving factors of degradation in Australian residential photovoltaic systems: A performance loss analysis', Sydney, Australia, presented at
, 2024, 'First approximations for cleaning of soil accumulated at Australian PV sites', Numazu, Japan, presented at
, 2024, 'Optimising electrical, financial, and environmental outcomes for photovoltaic module lifecycle management: an automated computational approach', Numazu, Japan, presented at
, 2024, 'Assessing degradation trends of residential photovoltaic systems and their drivers across Australia', Numazu, Japan, presented at
, 2024, 'Digital-twins of utility PV systems: PVsyst-based model limitations and implications', Numazu, Japan, presented at
, 2024, 'Variable performance loss rates at different times of the day in a utility-scale PV system', Numazu, Japan, presented at
, 2024, 'Machine learning applications for photovoltaic systems analysis', Queen's College, Oxford, United Kingdom, presented at
, 2024, 'Identifying distinct performance patterns in utility-scale photovoltaic plants using an unsupervised machine learning model', Vienna, Austria, presented at
, 2024, 'Photovoltaic cell defect classification from luminescence images: Embedding and clustering with unsupervised machine learning', Vienna, Austria, presented at
, 2024, 'Photovoltaic module array luminescence image preprocessing: Heuristic algorithms for perspective correction and cell segmentation', Vienna, Austria, presented at
, 2024, 'Interpretative Data Sonification: Using LLMs to Interpret Data and Generative Continuous Soundscapes at the Sydney Opera House', Porto, Portugal, pp. 102 - 109, presented at
, 2024, 'Deep learning-assisted extraction of photoluminescence and series resistance images from a single electroluminescence measurement', Seattle, USA, presented at
, 2024, 'Extracting the parameters of two-energy-level defects in silicon wafers using machine learning models', Seattle, USA, presented at
, 2024, 'Photovoltaic soiling from particulate matter across Australia', Canberra, Australia, presented at
, 2023, 'Automated photovoltaic module quality assessment from luminescence images: defect classification using unsupervised machine learning', Melbourne, Australia, presented at
, 2023, 'Advanced analysis of internal quantum efficiency measurements of GaAs solar cells using machine learning', Melbourne, Australia, presented at
, 2023, 'AI-driven extraction of spatially-resolved photoluminescence and series resistance from electroluminescence images', Melbourne, Australia, presented at
, 2023, 'Distortion correction for real-world solar panel images based on deep learning', Melbourne, Australia, presented at
, 2023, 'Enhancing luminescence images through deep learning-based point spread function correction', Melbourne, Australia, presented at
, 2023, 'Photovoltaic module performance and degradation analysis', Melbourne, Australia, presented at
, 2023, 'Using latent ordinary differential equation neural networks to predict the degradation of heterojunction PV modules at the end of damp heat tests', Melbourne, Australia, presented at
, 2023, 'Automated photovoltaic module end-of-life assessment', UNSW Sydney, Australia, presented at
, 2023, 'Automated photovoltaic module quality assessment: defect identification and classification from luminescence images using machine learning', Puerto Rico, presented at
, 2023, 'Predicting damp heat degradation in heterojunction modules using machine learning', Puerto Rico, presented at
, 2023, 'Predicting damp heat degradation in heterojunction modules using machine learning', Puerto Rico, presented at
, 2023, 'Using GPT-3 to Achieve Semantically Relevant Data Sonificiation for an Art Installation', Springer Nature, Brno, Czech Republic, pp. 212 - 227, presented at
, 2023, 'The Human-Built Environment-Natural Environment Relation-An Immersive Multisensory Exploration with 'System of a Sound'', in International Conference on Intelligent User Interfaces Proceedings IUI, pp. 8 - 11, http://dx.doi.org/10.1145/3581754.3584119
, 2023, 'Using GPT-3 to Achieve Semantically Relevant Data Sonificiation for an Art Installation', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 212 - 227, http://dx.doi.org/10.1007/978-3-031-29956-8_14
, 2022, 'Machine learning-powered module end-of-life decisions from luminescence images', Newcastle, Australia, presented at
, 2022, 'Machine learning-powered module end-of-life decision making based on luminescence images', Nagoya, Japan, presented at
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https://orcid.org/0000-0002-3345-5912