Advanced Statistical Learning on Short Term Load Process Forecasting

• URL: http://arxiv.org/abs/2110.09920v1
• Date: Tue, 19 Oct 2021 12:32:40 GMT
• Title: Advanced Statistical Learning on Short Term Load Process Forecasting
• Authors: Junjie Hu, Brenda L\'opez Cabrera, Awdesch Melzer
• Abstract summary: Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. 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.
• Score: 13.466565318976887
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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