This review reports a quantitative analysis across the deterministic photovoltaic (PV) power
forecasting approaches. Model accuracy tests from papers passing a set of selection criteria …

The surge of machine learning (ML) applications and increasing usage of data driven
approach for resilience enhancement provide great opportunities for applying ML …

Solar-powered water electrolysis can produce clean hydrogen for sustainable energy
systems. Accurate solar energy generation forecasts are necessary for system operation and …

Solar power integration in electrical grids is complicated due to dependence on volatile
weather conditions. To address this issue, continuous research and development is required …

Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …

Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a
promising research direction to intelligentize energy systems. With the massive smart meter …

Power modeling, widely applied for health monitoring and power prediction, is crucial for the
efficiency and reliability of Photovoltaic (PV) systems. The most common approach for power …

This paper presents a Deep Learning (DL) model designed for green hydrogen production
using a solar-powered water electrolyzer. The model operates in four phases, beginning …

The drive for smarter, greener, and more livable cities has led to research towards more
effective solar energy forecasting techniques and their integration into traditional power …

Anticipating changes in system inertia is crucial for maintaining the stability and reliability of
new power systems. While machine learning prediction models can be effective in this …