Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …

The world has relied on fossil fuel energy for a long time, producing many adverse effects.
Long-term fossil fuel dependency has increased carbon emissions and accelerated climate …

The primary goal of this study was to examine the relationship between fossil fuel energy,
electricity production from nuclear sources, renewable energy, CO2 emissions, and …

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 …

With the manufacturing reshoring to the US, increasing attention are focus on its energy
consumption and environmental effects and accurate prediction of carbon emissions is vital …

Carbon emissions play a crucial role in inducing global warming and climate change.
Accurate and stable carbon emissions forecasting is beneficial for formulating emissions …

As an industry that consumes a quarter of social energy and emits a third of greenhouse
gases, the construction industry has an important responsibility to achieve carbon peaking …

Iran has become one of the most CO 2 emitting countries during the last decades. The
country ranks after Japan and Germany in terms of CO 2 emissions. However, from an …

Microgrids (MGs) use renewable sources to meet the growing demand for energy with
increasing consumer needs and technological advancement. They operate independently …

Accurate and reliable load forecasting can ensure the safety and economy of power system
operation. To improve the accuracy of short-term power load forecasting, this paper adopts …