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

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. Compared 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 meantime, 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. ∗Corresponding author: [email protected] 1 ar X iv :2 10 8. 10 66 3v 1 [ cs .L G ] 2 4 A ug 2 02 1 Abbreviations TSF Time Series Forecasting SES Simple Exponential Smoothing ARIMA Autoregression Integrated Moving Average MES Multivariate Exponential Smoothing ARIMA Autoregression Moving Average MLP Multilayer Perceptron DCA Decline Curve Analysis BP backpropagation VAR Vector Autoregressive RNN Recurrent Neural Networks BVAR Bayesian VAR DRNN Deep RNN SVM Support Vector Machine LSTM Long Short-Term Memory SVR Support Vector Regression DSLTM Deep LSTM LSSVR Least Squares SVR ESN Echo State Network kNN k-Nearest Neighbour MAE Mean Absolute Error ANN Artificial Neural Networks MSE Mean Square Error UTS univariate Time Series RMSE Root MSE MTS Multivariate Time Series RMSPE Root Mean Square Percentage Error ES Exponential Smoothing MAPE Mean Absolute Percentage Error Keywords— Energy time series forecasting, Conventional forecasting methods, Machine learning, Deep learning, Energy management systems