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 …
A review on machine learning forecasting growth trends and their real-time applications in different energy systems
T Ahmad, H Chen - Sustainable Cities and Society, 2020 - Elsevier
Energy forecasting and planning play an important role in energy sector development and
policy formulation. The forecasting model selection mostly based on the availability of the …
policy formulation. The forecasting model selection mostly based on the availability of the …
[HTML][HTML] Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia
S Ghimire, B Bhandari, D Casillas-Perez… - … Applications of Artificial …, 2022 - Elsevier
This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global
Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with …
Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with …
Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms
This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction
problems, based on deep learning approaches. Predictive models are useful ploys in solar …
problems, based on deep learning approaches. Predictive models are useful ploys in solar …
[HTML][HTML] Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction
Global solar radiation (GSR) prediction plays an essential role in planning, controlling and
monitoring solar power systems. However, its stochastic behaviour is a significant challenge …
monitoring solar power systems. However, its stochastic behaviour is a significant challenge …
Solar radiation forecasting based on convolutional neural network and ensemble learning
Nowadays, we are moving forward to more sustainable energy production systems based
on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In …
on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In …
Feature selection in machine learning prediction systems for renewable energy applications
This paper focuses on feature selection problems that arise in renewable energy
applications. Feature selection is an important problem in machine learning, both in …
applications. Feature selection is an important problem in machine learning, both in …
Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data
M Ajith, M Martínez-Ramón - Applied Energy, 2021 - Elsevier
Solar irradiance forecasting has been gaining paramount importance in recent years due to
its impact on power grids. However, solar energy harvesting over shorter periods also brings …
its impact on power grids. However, solar energy harvesting over shorter periods also brings …
Review on photovoltaic power and solar resource forecasting: current status and trends
TC Carneiro, PCM de Carvalho… - Journal of Solar …, 2022 - asmedigitalcollection.asme.org
Photovoltaic (PV) power intermittence impacts electrical grid security and operation. Precise
PV power and solar irradiation forecasts have been investigated as significant reducers of …
PV power and solar irradiation forecasts have been investigated as significant reducers of …
Girasol, a sky imaging and global solar irradiance dataset
The energy available in a microgrid that is powered by solar energy is tightly related to the
weather conditions at the moment of generation. A very short-term forecast of solar …
weather conditions at the moment of generation. A very short-term forecast of solar …