| Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images - An in-depth Analysis Q Paletta, G Arbod, J Lasenby Solar Energy 224, 855-867, 2021 | 58 | 2021 |
| Convolutional neural networks applied to sky images for short-term solar irradiance forecasting Q Paletta, J Lasenby EU-PVSEC 2020, 2020 | 33 | 2020 |
| Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions Q Paletta, G Arbod, J Lasenby Applied Energy 336, 120818, 2023 | 32* | 2023 |
| ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy Q Paletta, A Hu, G Arbod, J Lasenby Applied Energy, 2022 | 32 | 2022 |
| Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey Y Nie, X Li, Q Paletta, M Aragon, A Scott, A Brandt Renewable and Sustainable Energy Reviews 189, 113977, 2024 | 20* | 2024 |
| SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting Q Paletta, A Hu, G Arbod, P Blanc, J Lasenby Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 16 | 2022 |
| Advances in solar forecasting: Computer vision with deep learning Q Paletta, G Terrén-Serrano, Y Nie, B Li, J Bieker, W Zhang, L Dubus, ... Advances in Applied Energy, 100150, 2023 | 15 | 2023 |
| A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications Q Paletta, J Lasenby NeurIPS 2020 - Tackling Climate Change with Machine Learning workshop, 2020 | 15 | 2020 |
| Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning Y Nie, Q Paletta, A Scott, LM Pomares, G Arbod, S Sgouridis, J Lasenby, ... Applied Energy 369, 123467, 2024 | 14* | 2024 |
| Cloud flow centring in sky and satellite images for deep solar forecasting Q Paletta, G Arbod, J Lasenby WCPEC-8, 2022 | 6 | 2022 |
| SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT Y Nie, E Zelikman, A Scott, Q Paletta, A Brandt Advances in Applied Energy 14, 100172, 2024 | 5* | 2024 |
| Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning Q Paletta, Y Nie, YM Saint-Drenan, B Le Saux Energy Conversion and Management 309, 118398, 2024 | 3 | 2024 |
| SkyImageNet: Towards a large-scale sky image dataset for solar power forecasting Y Nie, Q Paletta, S Wang ICLR 2024 Workshop on Tackling Climate Change with Machine Learning, 2024 | 3 | 2024 |
| Deep learning applied to sea surface semantic segmentation: Filtering sunglint from aerial imagery T Vrecica, Q Paletta, L Lenain ICML 2021 - Tackling Climate Change with Machine Learning workshop, 2021 | 1 | 2021 |
| SIRONA-: 1 a low-cost platform for lunar exploration E Pachoud, B Eich, E Jehanno, T Errabih, F Clouvel, J Klein, F Hübner, ... 7th Interplanetary CubeSat Workshop, 2018 | | 2018 |
Joan LasenbyProfessor of Image and Signal Analysis, Department of Engineering, University of Cambridge在 cam.ac.uk 的电子邮件经过验证
