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) …

The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …

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 …

Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy
sources into the grid as it provides accurate and timely information on the expected output of …

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 …

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

Prediction of solar irradiance is essential for minimizing energy costs and providing high
power quality in electrical power grids with distributed solar photovoltaic generations …

Solar photovoltaic plants are widely integrated into most countries worldwide. Due to the
ever-growing utilization of solar photovoltaic plants, either via grid-connection or stand …

Text mining is an emerging topic that advances the review of academic literature. This paper
presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power …

This paper designs a hybridized deep learning framework that integrates the Convolutional
Neural Network for pattern recognition with the Long Short-Term Memory Network for half …