Energy Consumption Forecasting in Korea Using Machine Learning Algorithms

Sun-Youn Shin and Han-Gyun Woo
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Sun-Youn Shin: Korea Energy Economics Institute, 405-11, Jongga-ro, Jung-gu, Ulsan 44543, Korea
Han-Gyun Woo: Ulsan National Institute of Science and Technology, Ulsan 44543, Korea

Energies, 2022, vol. 15, issue 13, 1-20

Abstract: In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data.

Keywords: Total Energy Supply; energy consumption; forecasting; deep learning; neural network; artificial intelligence; random forest; XGBoost; LSTM; Korea (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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