A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
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 …
Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine
Recently, many machine learning techniques have been successfully employed in
photovoltaic (PV) power output prediction because of their strong non-linear regression …
photovoltaic (PV) power output prediction because of their strong non-linear regression …
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 …
SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting
D Korkmaz - Applied Energy, 2021 - Elsevier
Photovoltaic (PV) power generation has high uncertainties due to the randomness and
imbalance nature of solar energy and meteorological parameters. Hence, accurate PV …
imbalance nature of solar energy and meteorological parameters. Hence, accurate PV …
Hybridization of hybrid structures for time series forecasting: A review
Achieving the desired accuracy in time series forecasting has become a binding domain,
and develo** a forecasting framework with a high degree of accuracy is one of the most …
and develo** a forecasting framework with a high degree of accuracy is one of the most …
Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm
J Wang, Y Zhou, Z Li - Applied Energy, 2022 - Elsevier
As the penetration rate of solar energy in the grid continues to enhance, solar power
photovoltaic generation forecasts have become an indispensable aspect of mechanism …
photovoltaic generation forecasts have become an indispensable aspect of mechanism …
Short-term photovoltaic power forecasting based on signal decomposition and machine learning optimization
Y Zhou, J Wang, Z Li, H Lu - Energy Conversion and Management, 2022 - Elsevier
Owing to the continuous increase in the proportion of solar generation accounting for the
total global generation, real-time management of solar power has become indispensable …
total global generation, real-time management of solar power has become indispensable …
An effective hybrid NARX-LSTM model for point and interval PV power forecasting
This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
[HTML][HTML] Short-term PV power forecasting using variational mode decomposition integrated with Ant colony optimization and neural network
Abstract In this paper, Artificial Neural Network (ANN) is integrated with data processing,
input variable selection, and external optimization techniques to forecast the day ahead …
input variable selection, and external optimization techniques to forecast the day ahead …