The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …

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 …

Electricity prices have rather complex features such as high volatility, high frequency,
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …

The cost of electricity and gas has a direct influence on the everyday routines of people who
rely on these resources to keep their businesses running. However, the value of electricity is …

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 …

This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and
Applications in Science, Engineering, Medicine and Finance has been fully updated and …

Many recent studies propose wavelet-based hydrological and water resources forecasting
models that have been incorrectly developed and that cannot properly be used for real …

The accurate prediction of energy price is critical to the energy market orientation, and it can
provide a reference for policymakers and market participants. In practice, energy prices are …

Electricity demand forecasting could prove to be a useful policy tool for decision-makers;
thus, accurate forecasting of electricity demand is valuable in allowing both power …