Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …

Solar power is expected to play a substantial role globally, due to it being one of the leading
renewable electricity sources for future use. Even though the use of solar irradiation to …

We propose a new architecture based on a fully connected feed-forward Artificial Neural
Network (ANN) model to estimate surface soil moisture from satellite images on a large …

In the context of forecasting for renewable energy, it is common to produce point forecasts
but it is also important to have information about the uncertainty of the forecast. To this …

Accurate electricity consumption forecasting is a challenging task for its unstable behavior
and influence mechanism based on multiple factors. In this study, a neural network …

Deep neural networks (DNN) are becoming increasingly relevant for probabilistic
forecasting because of their ability to estimate prediction intervals (PIs). Two different ways …

Abstract Machine learning is routinely used to forecast solar radiation from inputs, which are
forecasts of meteorological variables provided by numerical weather prediction (NWP) …

Rare class imbalance problems, which involve the classification of minority or rare class, are
difficult, because the size of the rare class is smaller than the majority class. Since majority …

Solar energy presents a promising solution to replace fossil-based energy sources,
mitigating global warming and climate change. However, solar energy faces socio …

Hyperspectral observations have become one of the most popular and powerful methods for
atmospheric remote sensing, and are widely used for temperature, gas, aerosol, and cloud …