Deep learning models for solar irradiance forecasting: A comprehensive review
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 …
[HTML][HTML] Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions
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 …
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 …
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] A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights
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 …
Short-term solar power predicting model based on multi-step CNN stacked LSTM technique
Variability in solar irradiance has an impact on the stability of solar systems and the grid's
safety. With the decreasing cost of solar panels and recent advancements in energy …
safety. With the decreasing cost of solar panels and recent advancements in energy …
Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: A review and new modeling results
We review the latest modeling techniques and propose new hybrid SAELSTM framework
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …
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 …
Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer
C Jiang, Q Zhu - Applied Energy, 2023 - Elsevier
The number of existing global solar radiation (GSR) observation stations is limited, and it is
challenging to meet the demand for scientific research and production. Different forecasting …
challenging to meet the demand for scientific research and production. Different forecasting …