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Energy demand pattern analysis in South Korea using hidden Markov model‐based classification

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  • Jaeyong Lee
  • Beom Seuk Hwang

Abstract

Understanding energy demand patterns in the residential sector is crucial for improving energy efficiency through demand‐side management. Load curve classification is a useful method for analyzing energy demand patterns. In this paper, we employ a hidden Markov model (HMM)‐based classification to residential load curves in South Korea. We also investigate how the number of hidden states affects classification performance by allowing HMM to train with a different number of hidden states for each class. We compare our HMM‐based method with several state‐of‐the‐art models and find that it outperforms other competing models in multiple datasets. Additionally, we use the fitted HMM model to make inferences about the load curves, gaining deeper insights into energy demand patterns.

Suggested Citation

  • Jaeyong Lee & Beom Seuk Hwang, 2024. "Energy demand pattern analysis in South Korea using hidden Markov model‐based classification," Asian Economic Journal, East Asian Economic Association, vol. 38(3), pages 404-428, September.
    • Handle: RePEc:bla:asiaec:v:38:y:2024:i:3:p:404-428
    DOI: 10.1111/asej.12338
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    References listed on IDEAS

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    1. Zhou, Jie & Song, Xinyuan & Sun, Liuquan, 2020. "Continuous time hidden Markov model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
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