[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 …
[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 …
SkyImageNet: Towards a large-scale sky image dataset for solar power forecasting
The variability of solar photovoltaic (PV) output, particularly that caused by rapidly changing
cloud dynamics, challenges the reliability of renewable energy systems. Solar forecasting …
cloud dynamics, challenges the reliability of renewable energy systems. Solar forecasting …
A newly developed model for estimating snow depth in ungauged areas
F Hashemireza, A Sharafati, T Raziei… - … of the Earth, Parts A/B/C, 2024 - Elsevier
The presence of snow significantly affects the hydrological cycle and soil moisture globally.
Nowadays, with the expansion of science, different satellites can measure snow depth all …
Nowadays, with the expansion of science, different satellites can measure snow depth all …
Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors
MJ Jang, E Oh - Sustainability, 2024 - mdpi.com
This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G)
operation strategy that focuses on the dynamic integration of charging station (CS) status to …
operation strategy that focuses on the dynamic integration of charging station (CS) status to …
Revolution in Image Data Collection: CycleGAN as a Dataset Generator
D Hindarto, ETE Handayani - Sinkron: jurnal dan penelitian teknik …, 2024 - polgan.ac.id
Computer vision, deep learning, and pattern recognition are just a few fields where image
data collection has become crucial. The Cycle Generative Adversarial Network has become …
data collection has become crucial. The Cycle Generative Adversarial Network has become …
[PDF][PDF] SatStreaks: Towards Supervised Learning for Delineating Satellite Streaks from Astronomical Images
S Chatterjee, P Kudeshia, N Kollo, MA Agowun… - assets.pubpub.org
Delineation of satellite streaks in astronomical images is an important aspect of ground
based space studies. While deep learning algorithms show promise, training and validation …
based space studies. While deep learning algorithms show promise, training and validation …