The literature of energy forecast is moving from the traditional statistical prediction methods
to today's more intelligent machine learning algorithms due to continuous development in …

This paper studies the Adaptive neural fuzzy inference systems (ANFIS) based on the fuzzy
model Sugeno, on the basis of the network is given the right to amend the value of the …

Better accuracy in short-term forecasting is required for intermediate planning for the
national target to reduce CO2 emissions. High stake climate change conventions need …

Global warming is one of the biggest challenges among the leaders and scientists from
developed and developing countries. Rapid industrialization and urbanization have given …

The amount of CO 2 emission has significantly increased because of the increase in
industrial production, usage of fossil fuels such as petroleum and coal which is a danger for …

The accurate prediction of daily carbon dioxide (CO 2) emissions is crucial for grasping the
real-time dynamics of CO 2 emissions and formulating emission reduction policies. The use …

Carbon dioxide (CO 2) emission has increased rapidly due to the predominant usage of
fossil fuels. The energy sector contributes a considerable amount towards the total share …

This study focuses to propose regression models to forecast the fossil fuel-related carbon
dioxide (FFCO 2) emissions in the BRICS (Brazil, the Russian Federation, India, China …

This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto
regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption …

The escalating levels of carbon dioxide (CO2) emissions represent the primary driver of
global warming, and addressing them is of paramount importance. Timely and accurate …