  Machine learning methods have been adopted in the literature as contenders to
conventional methods to solve the energy time series forecasting (TSF)
problems. Recently, deep learning methods have been emerged in the artificial
intelligence field attaining astonishing performance in a wide range of
applications. Yet, the evidence about their performance in to solve the energy
TSF problems, in terms of accuracy and computational requirements, is scanty.
Most of the review articles that handle the energy TSF problem are systematic
reviews, however, a qualitative and quantitative study for the energy TSF
problem is not yet available in the literature. The purpose of this paper is
twofold, first it provides a comprehensive analytical assessment for
conventional,machine learning, and deep learning methods that can be utilized
to solve various energy TSF problems. Second, the paper carries out an
empirical assessment for many selected methods through three real-world
datasets. These datasets related to electrical energy consumption problem,
natural gas problem, and electric power consumption of an individual household
problem.The first two problems are univariate TSF and the third problem is a
multivariate TSF. Com-pared to both conventional and machine learning
contenders, the deep learning methods attain a significant improvement in terms
of accuracy and forecasting horizons examined. In the mean-time, their
computational requirements are notably greater than other contenders.
Eventually,the paper identifies a number of challenges, potential research
directions, and recommendations to the research community may serve as a basis
for further research in the energy forecasting domain.
