The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent
nature of the solar resource highlights the importance of power forecasting for the grid …

With the increase in demand for energy, penetration of alternative sources of energy in the
power grid has increased. Photovoltaic (PV) energy is the most common and popular form of …

Photovoltaic (PV) solar power generation is always associated with uncertainties due to
solar irradiance and other weather parameters intermittency. This creates a huge barrier in …

Recently, many machine learning techniques have been successfully employed in
photovoltaic (PV) power output prediction because of their strong non-linear regression …

In the last two decades, renewable energy forecasting progressed toward the development
of advanced physical and statistical algorithms aiming at improving point and probabilistic …

The rapid increase in the penetration of renewable energy resources characterized by high
variability and uncertainty is bringing new challenges to the power system operation. To …

This paper presents a new analog ensemble method for day-ahead regional photovoltaic
(PV) power forecasting with hourly resolution. By utilizing open weather forecast and power …

In recent years, the penetration of photovoltaic (PV) generation in the energy mix of several
countries has significantly increased thanks to policies favoring development of renewables …

Machine learning on graphs is an important and omnipresent task for a vast variety of
applications including anomaly detection and dynamic network analysis. In this paper, a …

Photovoltaic power generation is gradually developing into a massive power industry with
the maturity of renewable energy power generation technologies. Photovoltaic power …