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arXiv · 2110.09920

Advanced Statistical Learning on Short Term Load Process Forecasting

Junjie Hu·Brenda López Cabrera·Awdesch Melzer
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Abstract

Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by having a nonlinear temporal dependence structure. We propose different statistical nonlinear models to manage these challenges of hard type datasets and forecast 15-min frequency electricity load up to 2-days ahead. We show that the Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) models applied to the production line of a chemical production facility outperform several other predictive models in terms of out-of-sample forecasting accuracy by the Diebold-Mariano (DM) test with several metrics. The predictive information is fundamental for the risk and production management of electricity consumers.

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Junjie Hu, Brenda López Cabrera, Awdesch Melzer. 2021-10-19. Advanced Statistical Learning on Short Term Load Process Forecasting. https://arxiv.org/abs/2110.09920

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