Recent advancements in data science and artificial intelligence, as well as the development
of clean and sustainable energy sources, have created numerous opportunities for energy …

The energy sector currently faces growing challenges related to increasing demand,
efficiency, a lack of analytics required for optimal management, and changing supply and …

As the landscape of electric power systems is transforming towards decentralization, small-
scale electric power systems have garnered increased attention. Meanwhile, the …

A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent
part is presented for renewable energy modeling and prediction. Beside the rule …

This paper presents a first step in the field of probabilistic forecasting of co-located wind and
photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast …

The work explores the opportunities offered by higher temperature heat transfer/heat storage
fluids, and higher temperature power cycles, in higher concentration solar thermal power …

In the past two decades, wind energy has been under fast development worldwide. The
dramatic increase of wind power penetration in electricity production has posed a big …

In order to enable the transition towards renewable energy sources, probabilistic energy
forecasting is of critical importance for incorporating volatile power sources such as solar …

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to
problems in the atmospheric sciences is likely post-processing of model output. This article …

Artificial intelligence (AI) and machine learning (ML) have become important tools for
environmental scientists and engineers, both in research and in applications. Although …