Electricity demand time series forecasting based on empirical mode decomposition and long short-term memory
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
18
Series
Energy Engineering: Journal of the Association of Energy Engineering, Volume 118, issue 6, pp. 1577-1594
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.Description
Publisher Copyright: © 2021, Tech Science Press. All rights reserved.
Keywords
Empirical mode decomposition, Load forecasting, Logistic regression (LR), LSTM, Machine learning, XGBoost
Other note
Citation
Taheri, S, Talebjedi, B & Laukkanen, T 2021, ' Electricity demand time series forecasting based on empirical mode decomposition and long short-term memory ', Energy Engineering: Journal of the Association of Energy Engineering, vol. 118, no. 6, pp. 1577-1594 . https://doi.org/10.32604/EE.2021.017795