[HTML][HTML] An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network
In recent years, the penetration of solar power at residential and utility levels has progressed
exponentially. However, due to its stochastic nature, the prediction of solar global horizontal …
exponentially. However, due to its stochastic nature, the prediction of solar global horizontal …
Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting
P Kumari, D Toshniwal - Applied Energy, 2021 - Elsevier
The volatile behavior of solar energy is the biggest challenge in its successful integration
with existing grid systems. Accurate global horizontal irradiance (GHI) forecasting can …
with existing grid systems. Accurate global horizontal irradiance (GHI) forecasting can …
Short-term solar radiation forecasting with a novel image processing-based deep learning approach
In this study, an image processing-based deep learning approach for short-term forecast of
solar radiation has been developed. For this purpose, firstly, cloud movements occurred …
solar radiation has been developed. For this purpose, firstly, cloud movements occurred …
Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network
Accurate forecasting is indispensable for improving solar renewables integration and
minimizing the effects of solar energy's intermittency. Existing research on time series solar …
minimizing the effects of solar energy's intermittency. Existing research on time series solar …
Hybrid deep neural model for hourly solar irradiance forecasting
Owing to integrating photovoltaic solar systems into power networks, accurate prediction of
solar irradiance plays an increasingly significant role in electric energy planning and …
solar irradiance plays an increasingly significant role in electric energy planning and …
A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting
H Acikgoz - Applied Energy, 2022 - Elsevier
In this study, a novel deep solar forecasting approach is proposed based on the complete
ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous …
ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous …
[HTML][HTML] 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 …
Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization …
Y Wang, C Zhang, Y Fu, L Suo, S Song, T Peng… - Energy, 2023 - Elsevier
Solar energy is highly economical and widespread in new energy applications, and
analyzing solar radiation information is an important part of solar photovoltaic power …
analyzing solar radiation information is an important part of solar photovoltaic power …
Forecasting hourly solar irradiance using hybrid wavelet transformation and Elman model in smart grid
With the integration of photovoltaic (PV) power into an electrical network, the complexity of
the grid management is increasing because of intermittent and fluctuation nature of solar …
the grid management is increasing because of intermittent and fluctuation nature of solar …
Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks
Accurate and reliable solar irradiance forecasting can bring significant benefits for managing
electricity generation and distributing modern smart grid. However, the characteristics of …
electricity generation and distributing modern smart grid. However, the characteristics of …
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