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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 …
Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy
Y Tang, K Yang, S Zhang, Z Zhang - Renewable and Sustainable Energy …, 2022 - Elsevier
Accurate forecasting of photovoltaic power is essential in the integration, operation, and
scheduling of hybrid grid systems. In particular, modeling for newly built photovoltaic sites is …
scheduling of hybrid grid systems. In particular, modeling for newly built photovoltaic sites is …
Hourly stepwise forecasting for solar irradiance using integrated hybrid models CNN-LSTM-MLP combined with error correction and VMD
Accurate and reliable solar irradiance forecasting is critical for distribution planning and
modern smart grid management and dispatch. However, due to the time series of solar …
modern smart grid management and dispatch. However, due to the time series of solar …
[HTML][HTML] Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions
A Djaafari, A Ibrahim, N Bailek, K Bouchouicha… - Energy Reports, 2022 - Elsevier
Although solar energy harnessing capacity varies considerably based on the employed
solar energy technology and the meteorological conditions, accurate direct normal …
solar energy technology and the meteorological conditions, accurate direct normal …
An edge-AI based forecasting approach for improving smart microgrid efficiency
L Lv, Z Wu, L Zhang, BB Gupta… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Smart Grid 2.0 is the energy Internet based on advanced metering infrastructure and
distributed systems that require an instantaneous two-way flow of energy information. Edge …
distributed systems that require an instantaneous two-way flow of energy information. Edge …
[HTML][HTML] Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data
M Perera, J De Hoog, K Bandara, D Senanayake… - Applied Energy, 2024 - Elsevier
Regional solar power forecasting, which involves predicting the total power generation from
all rooftop photovoltaic (PV) systems in a region holds significant importance for various …
all rooftop photovoltaic (PV) systems in a region holds significant importance for various …
[HTML][HTML] A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights
BO Abisoye, Y Sun, W Zenghui - Renewable Energy Focus, 2024 - Elsevier
The efforts to revolutionize electric power generation and produce clean and sustainable
electricity have led to the exploration of renewable energy systems (RES). This form of …
electricity have led to the exploration of renewable energy systems (RES). This form of …
Hybrid Inception-embedded deep neural network ResNet for short and medium-term PV-Wind forecasting
Accurate and consistent forecasting of regional wind power is essential for efficient
scheduling and maximizing the utilization of renewable energy in the power grid. Medium …
scheduling and maximizing the utilization of renewable energy in the power grid. Medium …
Investigating the power of LSTM-based models in solar energy forecasting
NLM Jailani, JK Dhanasegaran, G Alkawsi… - Processes, 2023 - mdpi.com
Solar is a significant renewable energy source. Solar energy can provide for the world's
energy needs while minimizing global warming from traditional sources. Forecasting the …
energy needs while minimizing global warming from traditional sources. Forecasting the …
Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention
With the rapid development of high-performance computing technology, data-driven models,
especially deep learning models, are being used increasingly for solar radiation prediction …
especially deep learning models, are being used increasingly for solar radiation prediction …