Deep learning models for solar irradiance forecasting: A comprehensive review
P Kumari, D Toshniwal - Journal of Cleaner Production, 2021 - Elsevier
The growing human population in this modern society hugely depends on the energy to
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions
Spatiotemporal forecasting (STF) extends traditional time series forecasting or spatial
interpolation problem to space and time dimensions. Here, we review the statistical, physical …
interpolation problem to space and time dimensions. Here, we review the statistical, physical …
Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting
Solar photovoltaic (PV) power forecasting has become an important issue with regard to the
power grid in terms of the effective integration of large-scale PV plants. As the main …
power grid in terms of the effective integration of large-scale PV plants. As the main …
Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation
Solar radiation (SR) knowledge plays a vital role in the design, modelling, and operation of
solar energy conversion systems and future energy investment policies of the governments …
solar energy conversion systems and future energy investment policies of the governments …
A guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES)
D Yang - Journal of Renewable and Sustainable Energy, 2019 - pubs.aip.org
Over the past decade, significant progress in solar forecasting has been made.
Nevertheless, there are concerns about duplication, long-term value, and reproducibility; this …
Nevertheless, there are concerns about duplication, long-term value, and reproducibility; this …
Review on photovoltaic power and solar resource forecasting: current status and trends
TC Carneiro, PCM de Carvalho… - Journal of Solar …, 2022 - asmedigitalcollection.asme.org
Photovoltaic (PV) power intermittence impacts electrical grid security and operation. Precise
PV power and solar irradiation forecasts have been investigated as significant reducers of …
PV power and solar irradiation forecasts have been investigated as significant reducers of …
A review on neural network based models for short term solar irradiance forecasting
The accuracy of solar energy forecasting is critical for power system planning, management,
and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant …
and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant …
Integrated MFFNN-MVO approach for PV solar power forecasting considering thermal effects and environmental conditions
Photovoltaic (PV) panels are commonly used as clean energy sources. However, their
performance is sensitive to environmental conditions. In this work, a hybrid model of artificial …
performance is sensitive to environmental conditions. In this work, a hybrid model of artificial …
A novel composite neural network based method for wind and solar power forecasting in microgrids
Nowadays, wind and solar power generation have a major impact in many microgrid hybrid
energy systems based on their cost and pollution. On the other hand, accurate forecasting of …
energy systems based on their cost and pollution. On the other hand, accurate forecasting of …