Of the various renewable energy resources, wind power is widely recognized as one of the
most promising. The management of wind farms and electricity systems can benefit greatly …

Improving the accuracy of crude oil price forecasting is helpful for stabilizing financial
markets and oil import and export trade. However, the extant models rarely focus on the …

The current deep concerns on energy independence and global society's security at the face
of climate change have empowered the new “green energy” paradigm and led to a rapid …

The emerging need for risk-aware operational decisions on power systems calls for the
development of accurate probabilistic load forecasting methods. To serve this purpose …

A global horizontal irradiation prediction (from 1 h to 6 h) is performed using 2 persistence
models (simple and “smart” ones) and 4 machine learning tools belonging to the regression …

As global solar radiation forecasting is a very important challenge, several methods are
devoted to this goal with different levels of accuracy and confidence. In this study we …

Algorithmic trading requires tuning hyperparameters to fit the time series data; however, it
often suffers from overfitting of data that can lead to loss of money in action. Further, only a …

Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving
Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and …

A major impact of globalization has been the information flow across the financial markets
rendering them vulnerable to financial contagion. Research has focused on network …

Bootstrap procedures are useful to obtain forecast densities for both returns and volatilities
in the context of generalized autoregressive conditional heteroscedasticity models. In this …