Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models

TL;DR

This paper provides a comprehensive analysis and empirical evaluation of conventional, machine learning, and deep learning models for energy time series forecasting, highlighting deep learning's superior accuracy but higher computational costs.

Contribution

It offers the first combined analytical and empirical assessment of various models, including deep learning, for energy TSF problems across multiple real-world datasets.

Findings

Deep learning models significantly improve forecasting accuracy.

Deep learning methods require higher computational resources.

Traditional models are less accurate but more efficient.

Abstract

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.