# Advanced Statistical Learning on Short Term Load Process Forecasting

arXiv: https://arxiv.org/abs/2110.09920 · HTML: https://mlsanity.com/paper/2110.09920/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 42 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Principal Component Analysis in an Asymmetric Norm](https://mlsanity.com/paper/1401.3229/) (2014)

## Abstract (arXiv)

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.