[HTML][HTML] 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… - Applied Energy, 2024 - Elsevier
Solar forecasting from ground-based sky images has shown great promise in reducing the
uncertainty in solar power generation. With more and more sky image datasets available in …

[HTML][HTML] Skygpt: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained videogpt

Y Nie, E Zelikman, A Scott, Q Paletta… - Advances in Applied …, 2024 - Elsevier
The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud
dynamics, hinders the transition to reliable renewable energy systems. Information on future …

[HTML][HTML] Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning

Q Paletta, Y Nie, YM Saint-Drenan… - Energy Conversion and …, 2024 - Elsevier
Forecasting solar energy from cloud cover observations is crucial to truly anticipate future
changes in power supply. On an intra-hour timescale, ground-level sky cameras located …

Triple-Stream Temporal Model (TSTM): Enhancing Solar Energy Forecasting Using Hybrid Deep Learning Methods

G Pranav, N Karuppiah, P Mounica… - … for Advancement in …, 2024 - ieeexplore.ieee.org
Solar energy forecasting is a very significant part of optimizing the solar power management
and integrating it into the energy grid. The accurate power output of the solar energy helps …

Vision-Based Solar Forecasting with Deep Learning

Q Paletta - 2024 - repository.cam.ac.uk
Solar power is expected to play a leading role in the current electrification of our economy
and its shift towards a low-carbon energy supply. This source of energy has numerous …