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 …
Impact of dust accumulation on photovoltaic panels: a review paper
Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective
renewable energy resource due to their environmental and economic merits. However, PV …
renewable energy resource due to their environmental and economic merits. However, PV …
An LSTM short-term solar irradiance forecasting under complicated weather conditions
Y Yu, J Cao, J Zhu - IEEE Access, 2019 - ieeexplore.ieee.org
Complicated weather conditions lead to intermittent, random and volatility in photovoltaic
(PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is …
(PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is …
SKIPP'D: A SKy Images and Photovoltaic Power Generation Dataset for short-term solar forecasting
Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the
intermittent nature of solar power. Sky-image-based solar forecasting using deep learning …
intermittent nature of solar power. Sky-image-based solar forecasting using deep learning …
A robust auto encoder-gated recurrent unit (AE-GRU) based deep learning approach for short term solar power forecasting
The increasing presence of solar power plants shows its potency as one of the key
renewable energy resource to fulfill energy needs of the community. This increasing …
renewable energy resource to fulfill energy needs of the community. This increasing …
[HTML][HTML] ECLIPSE: Envisioning cloud induced perturbations in solar energy
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation
of its intermittency. A promising approach to forecasting the temporal variability of solar …
of its intermittency. A promising approach to forecasting the temporal variability of solar …
Diffusion models for high-resolution solar forecasts
Y Hatanaka, Y Glaser, G Galgon, G Torri… - arxiv preprint arxiv …, 2023 - arxiv.org
Forecasting future weather and climate is inherently difficult. Machine learning offers new
approaches to increase the accuracy and computational efficiency of forecasts, but current …
approaches to increase the accuracy and computational efficiency of forecasts, but current …
A CNN‐BiLSTM based deep learning model for mid‐term solar radiation prediction
The penetrations of solar power plants are increasing their presence worldwide. The solar
power plants have uncertain power output as its output depends on solar radiation, which is …
power plants have uncertain power output as its output depends on solar radiation, which is …
[HTML][HTML] Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging
BJ Martins, A Cerentini, SL Mantelli, TZL Chaves… - Solar Energy …, 2022 - Elsevier
Nowcasting of solar energy considering clouds is important for photovoltaic solar plants and
distributed systems. Clouds present a challenge for modeling, due to constant changes in …
distributed systems. Clouds present a challenge for modeling, due to constant changes in …
Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network
This paper aims to develop the long short-term memory (LSTM) network modelling strategy
based on deep learning principles, tailored for the very short-term, near-real-time global …
based on deep learning principles, tailored for the very short-term, near-real-time global …