The growing human population in this modern society hugely depends on the energy to
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …

Presently, deep learning models are an alternative solution for predicting solar energy
because of their accuracy. The present study reviews deep learning models for handling …

The integration of PV power brings great economic and environmental benefits. However,
the high penetration of PV power may challenge the planning and operation of the existing …

In order to identify power production and demand in realtime for efficient and dependable
management for diverse renewable energy systems, precise and intuitive renewable energy …

Accurate prediction of solar energy is an important issue for photovoltaic power plants to
enable early participation in energy auction industries and cost-effective resource planning …

Photovoltaic (PV) power generation has high uncertainties due to the randomness and
imbalance nature of solar energy and meteorological parameters. Hence, accurate PV …

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 …

Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of
the power grid. However, PV power has great fluctuations due to various meteorological …

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 …

Energy crisis and climate change are the major concerns which has led to a significant
growth in the renewable energy resources which includes mainly the solar and wind power …