Electricity demand time series forecasting based on empirical mode decomposition and long short-term memory

Saman Taheri, Behnam Talebjedi*, Timo Laukkanen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
391 Downloads (Pure)

Abstract

Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs). For each of the derived IMFs, a different LSTM model is trained. Finally, the outputs of all the individual LSTM learners are fed to a meta-learner to provide an aggregated output for the energy demand prediction. The suggested methodology is applied to the California ISO dataset to demonstrate its applicability. Additionally, we compare the output of the proposed algorithm to a single LSTM and two state-of-the-art data-driven models, specifically XGBoost, and logistic regression (LR). The proposed hybrid model outperforms single LSTM, LR, and XGBoost by, 35.19%, 54%, and 49.25% for short-term, and 36.3%, 34.04%, 32% for long-term prediction in mean absolute percentage error, respectively.

Original languageEnglish
Pages (from-to)1577-1594
Number of pages18
JournalEnergy Engineering: Journal of the Association of Energy Engineering
Volume118
Issue number6
Early online date10 Sept 2021
DOIs
Publication statusPublished - 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Empirical mode decomposition
  • Load forecasting
  • Logistic regression (LR)
  • LSTM
  • Machine learning
  • XGBoost

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