[HTML][HTML] Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning
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 …
changes in power supply. On an intra-hour timescale, ground-level sky cameras located …
[HTML][HTML] Sky image-based solar forecasting using deep learning with heterogeneous multi-location data: Dataset fusion versus transfer learning
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 …
uncertainty in solar power generation. With more and more sky image datasets available in …
[HTML][HTML] Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions
Integrating large proportions of intermittent renewable energy sources into electric grids is
challenging. A well-established approach aimed at addressing this difficulty involves the …
challenging. A well-established approach aimed at addressing this difficulty involves the …
Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?
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 open …
uncertainty in solar power generation. With more and more sky image datasets open …
[HTML][HTML] IrradianceNet: Spatiotemporal deep learning model for satellite-derived solar irradiance short-term forecasting
The presence of clouds is widely identified as the primary uncertainty in current surface solar
global horizontal irradiance (GHI) forecasts. Despite a wealth of historical satellite-derived …
global horizontal irradiance (GHI) forecasts. Despite a wealth of historical satellite-derived …
[HTML][HTML] Advances in solar forecasting: Computer vision with deep learning
Renewable energy forecasting is crucial for integrating variable energy sources into the grid.
It allows power systems to address the intermittency of the energy supply at different …
It allows power systems to address the intermittency of the energy supply at different …
Benchmarking of deep learning irradiance forecasting models from sky images–An in-depth analysis
A number of industrial applications, such as smart grids, power plant operation, hybrid
system management or energy trading, could benefit from improved short-term solar …
system management or energy trading, could benefit from improved short-term solar …
[HTML][HTML] All sky imaging-based short-term solar irradiance forecasting with Long Short-Term Memory networks
The intermittent nature of solar irradiance, primarily due to cloud movements, leads to rapid
short-term fluctuations in the power output of photovoltaic (PV) systems. These fluctuations …
short-term fluctuations in the power output of photovoltaic (PV) systems. These fluctuations …
A flexible and robust deep learning-based system for solar irradiance forecasting
II Prado-Rujas, A García-Dopico, E Serrano… - IEEE …, 2021 - ieeexplore.ieee.org
Most studies about the solar forecasting topic do not analyze and exploit the temporal and
spatial components that are inherent to such a task. Furthermore, they mostly focus just on …
spatial components that are inherent to such a task. Furthermore, they mostly focus just on …
Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting
With the ever-increased penetration of solar energy in the power grid, solar photovoltaic
forecasting has become an indispensable aspect in maintaining power system stability and …
forecasting has become an indispensable aspect in maintaining power system stability and …