This study presents a broad view of the current state of the art of ML applications in the
manufacturing sectors that have a considerable impact on sustainability and the …

Global solar radiation information is the basis for many solar energy utilizations as well as
for economic and environmental considerations. However, because solar-radiation …

For better estimation of renewable environmental friendly and carbon-free energy resources,
precise prediction of solar energy is very essential. However, accurate prediction of solar …

In this study, a novel deep solar forecasting approach is proposed based on the complete
ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous …

Abstract Artificial Neural Networks (ANNs) are proposed for sizing and simulating a clean
energy community (CEC) that employs a PV-wind hybrid system, coupled with energy …

Providing an accurate and reliable solar radiation prediction is highly significant for optimal
design and management of thermal and solar photovoltaic systems. It is massively essential …

Optimal utilisation of the sun's freely available energy to generate electricity requires efficient
predictive models of global solar radiation (GSR). These are necessary to provide solar …

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 …

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 …

Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation
of power stations and ensure users' electricity consumption. As a good forecasting tool …