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Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks

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Center PERSEE, MINES ParisTech, PSL University, 1 Rue Claude Daunesse, 06904 Sophia Antipolis, France
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Ubitech Energy, Koningin Astridlaan 59b, 1780 Brussels, Belgium
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Department of Electrical & Computer Engineering, University of Patras, 26504 Patras, Greece
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Department of Electrical and Electronic Engineering Educators, A.S.PE.T.E.—School of Pedagogical and Technological Education, N. Heraklion, 14121 Athens, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: José Antonio Domínguez-Navarro
Energies 2021, 14(14), 4107; https://doi.org/10.3390/en14144107
Received: 20 May 2021 / Revised: 29 June 2021 / Accepted: 2 July 2021 / Published: 7 July 2021
Short-term electricity load forecasting is key to the safe, reliable, and economical operation of power systems. An important challenge that arises with high-frequency load series, e.g., hourly load, is how to deal with the complex seasonal patterns that are present. Standard approaches suggest either removing seasonality prior to modeling or applying time series decomposition. This work proposes a hybrid approach that combines Singular Spectrum Analysis (SSA)-based decomposition and Artificial Neural Networks (ANNs) for day-ahead hourly load forecasting. First, the trajectory matrix of the time series is constructed and decomposed into trend, oscillating, and noise components. Next, the extracted components are employed as exogenous regressors in a global forecasting model, comprising either a Multilayer Perceptron (MLP) or a Long Short-Term Memory (LSTM) predictive layer. The model is further extended to include exogenous features, e.g., weather forecasts, transformed via parallel dense layers. The predictive performance is evaluated on two real-world datasets, controlling for the effect of exogenous features on predictive accuracy. The results showcase that the decomposition step improves the relative performance for ANN models, with the combination of LSTM and SAA providing the best overall performance. View Full-Text
Keywords: LSTM; short-term load forecasting; singular spectrum analysis; time series decomposition LSTM; short-term load forecasting; singular spectrum analysis; time series decomposition
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MDPI and ACS Style

Stratigakos, A.; Bachoumis, A.; Vita, V.; Zafiropoulos, E. Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks. Energies 2021, 14, 4107. https://doi.org/10.3390/en14144107

AMA Style

Stratigakos A, Bachoumis A, Vita V, Zafiropoulos E. Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks. Energies. 2021; 14(14):4107. https://doi.org/10.3390/en14144107

Chicago/Turabian Style

Stratigakos, Akylas, Athanasios Bachoumis, Vasiliki Vita, and Elias Zafiropoulos. 2021. "Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks" Energies 14, no. 14: 4107. https://doi.org/10.3390/en14144107

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