As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …

With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …

Gold price has always played an important role in the world economy and finance. In order
to predict the gold price more accurately, this paper proposes a novel decomposition …

Accurate load forecasting is helpful for optimizing the use of power resources. To this end,
this investigation proposes a hybrid model for short-term load forecasting, namely the RF …

The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …

This paper develops a novel short-term load forecasting model that hybridizes several
machine learning methods, such as support vector regression (SVR), grey catastrophe (GC …

Fault diagnosis plays an important role in actual production activities. As large amounts of
data can be collected efficiently and economically, data-driven methods based on deep …

Uncertainty modeling of renewable energy sources, load demand, electricity price, etc.
create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting …

During the past century, energy consumption and associated greenhouse gas emissions
have increased drastically due to a wide variety of factors including both technological and …

Accurately forecasting crude oil prices has drawn much attention from researchers,
investors, producers, and consumers. However, the complexity of crude oil prices makes it a …