This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain
management (SCM) through a systematic review of the existing literature. To address the …

Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …

Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …

Renewable and sustainable energy systems and policies have globally been promoted to
transition from fossil fuel sources to environmentally friendly renewable energy sources such …

This paper presents a comprehensive review of machine learning (ML) based approaches,
especially artificial neural networks (ANNs) in time series data prediction problems …

The back propagation neural network (BPNN) can easily fall into the local minimum point in
time series forecasting. A hybrid approach that combines the adaptive differential evolution …

Accurate electricity consumption forecast has primary importance in the energy planning of
the developing countries. During the last decade several new techniques are being used for …

Energy is vital for sustainable development of any nation–be it social, economic or
environment. In the past decade energy consumption has increased exponentially globally …

Energy consumption is on the rise in developing economies. In order to improve present and
future energy supplies, forecasting energy demands is essential. However, lack of accurate …

The rapid development of big data technologies and the Internet provides a rich mine of
online big data (eg, trend spotting) that can be helpful in predicting oil consumption—an …