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

Akylas Stratigakos, Athanasios Bachoumis, Vasiliki Vita and Elias Zafiropoulos
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Akylas Stratigakos: Center PERSEE, MINES ParisTech, PSL University, 1 Rue Claude Daunesse, 06904 Sophia Antipolis, France
Athanasios Bachoumis: Ubitech Energy, Koningin Astridlaan 59b, 1780 Brussels, Belgium
Vasiliki Vita: Department of Electrical and Electronic Engineering Educators, A.S.PE.T.E.—School of Pedagogical and Technological Education, N. Heraklion, 14121 Athens, Greece
Elias Zafiropoulos: Ubitech Energy, Koningin Astridlaan 59b, 1780 Brussels, Belgium

Energies, 2021, vol. 14, issue 14, 1-13

Abstract: 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.

Keywords: LSTM; short-term load forecasting; singular spectrum analysis; time series decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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