ScholarWorks@중앙대학교

초록

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.

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