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 …

[HTML][HTML] A survey of machine learning models in renewable energy predictions

JP Lai, YM Chang, CH Chen, PF Pai - Applied Sciences, 2020 - mdpi.com
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 …

Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models

A Agga, A Abbou, M Labbadi, Y El Houm - Renewable Energy, 2021 - Elsevier
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 …

Green hydrogen production ensemble forecasting based on hybrid dynamic optimization algorithm

AA Alhussan, ESM El-Kenawy, MA Saeed… - Frontiers in Energy …, 2023 - frontiersin.org
Solar-powered water electrolysis can produce clean hydrogen for sustainable energy
systems. Accurate solar energy generation forecasts are necessary for system operation and …

Investigating photovoltaic solar power output forecasting using machine learning algorithms

Y Essam, AN Ahmed, R Ramli, KW Chau… - Engineering …, 2022 - Taylor & Francis
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 …

[PDF][PDF] A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India

J Sharma, S Soni, P Paliwal, S Saboor… - Energy Science & …, 2022 - Wiley Online Library
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 …

[HTML][HTML] Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions

AK Tripathi, M Aruna, PV Elumalai, K Karthik… - Case Studies in Thermal …, 2024 - Elsevier
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 …

[HTML][HTML] Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach

A Dairi, F Harrou, Y Sun, S Khadraoui - Applied Sciences, 2020 - mdpi.com
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 …

A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches

A Sabadus, R Blaga, SM Hategan, D Calinoiu… - Renewable Energy, 2024 - Elsevier
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 …

[HTML][HTML] Machine learning approaches to predict electricity production from renewable energy sources

A Krechowicz, M Krechowicz, K Poczeta - Energies, 2022 - mdpi.com
Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …