The need to turn to more environmentally friendly sources of energy has led energy systems
to focus on renewable sources of energy. Wind power has been a widely used source of …

With the increase of converter-fed devices such as renewable energy sources, energy
storage, electric vehicles, direct current transmissions and power electronic loads in the …

Accurate regional wind power prediction is of great significance to the wind farm clusters
integration and the economic dispatch of the regional power grid. The complex …

Wind speed forecasting is still a challenge due to the stochastic and highly varying
characteristics of wind. In this paper, a graph deep learning model is proposed to learn the …

Accurate wind farm cluster power forecasting is of great significance for the safe operation of
the power system with high wind power penetration. However, most of the current neural …

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 …

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

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 …

Data Science for Wind Energy provides an in-depth discussion on how data science
methods can improve decision making for wind energy applications, near-ground wind field …

The ever-increasing number of wind farms has brought both challenges and opportunities in
the development of wind power forecasting techniques to take advantage of …