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Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review
J Tian, R Ooka, D Lee - Journal of Cleaner Production, 2023 - Elsevier
Solar energy has been rapidly utilized in urban environments owing to its significant
potential to fulfill the energy demand. The precise forecasting of solar energy, including solar …
potential to fulfill the energy demand. The precise forecasting of solar energy, including solar …
[HTML][HTML] 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 …
Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models
Global electricity consumption has raised in the last century due to many reasons such as
the increase in human population and technological development. To keep up with this …
the increase in human population and technological development. To keep up with this …
Green hydrogen production ensemble forecasting based on hybrid dynamic optimization algorithm
Solar-powered water electrolysis can produce clean hydrogen for sustainable energy
systems. Accurate solar energy generation forecasts are necessary for system operation and …
systems. Accurate solar energy generation forecasts are necessary for system operation and …
Investigating photovoltaic solar power output forecasting using machine learning algorithms
Solar power integration in electrical grids is complicated due to dependence on volatile
weather conditions. To address this issue, continuous research and development is required …
weather conditions. To address this issue, continuous research and development is required …
[PDF][PDF] A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India
Solar photovoltaic (PV) power is emerging as one of the most viable renewable energy
sources. The recent enhancements in the integration of renewable energy sources into the …
sources. The recent enhancements in the integration of renewable energy sources into the …
[HTML][HTML] Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions
Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their
power output often faces uncertainties due to dynamic weather conditions. In this study, a …
power output often faces uncertainties due to dynamic weather conditions. In this study, a …
[HTML][HTML] Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach
The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are
critical to efficiently managing their integration in smart grids, delivery, and storage. This …
critical to efficiently managing their integration in smart grids, delivery, and storage. This …
A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches
This review reports a quantitative analysis across the deterministic photovoltaic (PV) power
forecasting approaches. Model accuracy tests from papers passing a set of selection criteria …
forecasting approaches. Model accuracy tests from papers passing a set of selection criteria …
[HTML][HTML] Machine learning approaches to predict electricity production from renewable energy sources
Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
green house emissions, electricity generation from Renewable Energy Sources (RES) is …