[HTML][HTML] A review and taxonomy of wind and solar energy forecasting methods based on deep learning
G Alkhayat, R Mehmood - Energy and AI, 2021 - Elsevier
Renewable energy is essential for planet sustainability. Renewable energy output
forecasting has a significant impact on making decisions related to operating and managing …
forecasting has a significant impact on making decisions related to operating and managing …
[HTML][HTML] Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects
This article presents a review of current advances and prospects in the field of forecasting
renewable energy generation using machine learning (ML) and deep learning (DL) …
renewable energy generation using machine learning (ML) and deep learning (DL) …
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 …
An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction
Accurate prediction of wind speed is of great significance to the stable operation of wind
power equipment. In this study, a hybrid deep learning model based on convolutional neural …
power equipment. In this study, a hybrid deep learning model based on convolutional neural …
Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization
This study proposes an effective combined model system for wind speed forecasting tasks.
In this model,(a) improved hybrid time series decomposition strategy (HTD) is developed to …
In this model,(a) improved hybrid time series decomposition strategy (HTD) is developed to …
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 …
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 …
Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad
The growing threat of global climate change stemming from the huge carbon footprint left
behind by fossil fuels has prompted interest in exploring and utilizing renewable energy …
behind by fossil fuels has prompted interest in exploring and utilizing renewable energy …
A novel method based on time series ensemble model for hourly photovoltaic power prediction
Photovoltaic (PV) power generation technology is more and more widely used in smart
grids. Accurate prediction of PV power is very important for managing and planning of the …
grids. Accurate prediction of PV power is very important for managing and planning of the …
PV power forecasting based on data-driven models: a review
Accurate PV power forecasting techniques are a prerequisite for the optimal management of
the grid and its stability. This paper presents a review of the recent developments in the field …
the grid and its stability. This paper presents a review of the recent developments in the field …