Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting

In this paper we propose a sequence-to-sequence machine learning architecture for time-series forecasting based on recurrent neural networks. This architecture can be used as a general purpose forecasting method and is evaluated for the application of short-term electric load forecasting in this pap...

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Bibliographic Details
Published in2018 IEEE 16th International Conference on Industrial Informatics (INDIN) pp. 673 - 679
Main Authors Wilms, Henning, Cupelli, Marco, Monti, Antonello
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Auto-Regressive
Computer architecture
Decoding
Forecasting
Load forecasting
Logic gates
Neurons
Recurrent neural networks
Regression
STLF
Time-Series Forecasting
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Summary:In this paper we propose a sequence-to-sequence machine learning architecture for time-series forecasting based on recurrent neural networks. This architecture can be used as a general purpose forecasting method and is evaluated for the application of short-term electric load forecasting in this paper. The proposed sequence-to-sequence architecture 1 combines elements of auto-regressive forecasting techniques with multivariate regression by including exogenous variables for each forecasted time step as well as previous values when inferring forecasts. We assess the proposed architecture on a load data set provided by the Global Energy Forecasting Competition. The conclusion is that it outperforms other machine learning forecasting techniques as well as time-series analysis methods. 1 The implementation is available at: https://github.com/HenWil13/ieeeINDIN18
ISSN:2378-363X
DOI:10.1109/INDIN.2018.8471953