Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey
Sky image-based solar forecasting using deep learning has been recognized as a
promising approach in reducing the uncertainty of solar power generation. However, a major …
promising approach in reducing the uncertainty of solar power generation. However, a major …
A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence
Solar energy forecasting is essential for the effective integration of solar power into electricity
grids and the optimal management of renewable energy resources. Distinguishing itself from …
grids and the optimal management of renewable energy resources. Distinguishing itself from …
[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] SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT
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 …
dynamics, hinders the transition to reliable renewable energy systems. Information on future …
[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 …
Improved satellite-based intra-day solar forecasting with a chain of deep learning models
Satellite data and satellite-derived irradiance products have been extensively used in solar
forecasting to better capture the spatio-temporal variations of solar irradiance. However, the …
forecasting to better capture the spatio-temporal variations of solar irradiance. However, the …
On the use of sky images for intra-hour solar forecasting benchmarking: Comparison of indirect and direct approaches
The transient stability of the grid is challenged by short-term photovoltaic output fluctuations,
which are mainly caused by local clouds. To address this issue, intra-hour solar forecasting …
which are mainly caused by local clouds. To address this issue, intra-hour solar forecasting …
[HTML][HTML] Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network
The burgeoning proliferation of integrated energy systems has fostered an unprecedented
degree of coupling among various energy streams, thereby elevating the necessity for …
degree of coupling among various energy streams, thereby elevating the necessity for …
Capturing the diversity of mesoscale trade wind cumuli using complementary approaches from self‐supervised deep learning
D Chatterjee, S Schnitt, P Bigalke… - Geophysical …, 2024 - Wiley Online Library
At mesoscale, trade wind clouds organize with various spatial arrangements, shaping their
effect on Earth's energy budget. Representing their fine‐scale dynamics even at 1 km scale …
effect on Earth's energy budget. Representing their fine‐scale dynamics even at 1 km scale …
Effectiveness of forecasters based on Neural Networks for Energy Management in Zero Energy Buildings
IA Hernandez-Robles, X González-Ramírez… - Energy and …, 2024 - Elsevier
Energy management is an important challenge in Zero Energy Buildings (ZEB) with
Photovoltaic (PV) generation systems. Measuring, Forecasting and Controlling Energy are …
Photovoltaic (PV) generation systems. Measuring, Forecasting and Controlling Energy are …