Faster and Cheaper Energy Demand Forecasting at Scale

Fabien Bernier, Matthieu Jimenez, Maxime Cordy, YVES LE TRAON

Keywords: machine learning lightness, power consumption, forecasting, transformer

TL;DR: We propose an approach to forecast seasonal time series much faster; industrial case with power consumption.

Abstract:

Energy demand forecasting is one of the most challenging tasks for grids operators.
Many approaches have been suggested over the years to tackle it. Yet, those still remain too expensive to train in terms of both time and computational resources, hindering their adoption as customers behaviors are continuously evolving.

We introduce Transplit, a new lightweight transformer-based model, which significantly decreases this cost by exploiting the seasonality property and learning typical days of power demand. We show that Transplit can be run efficiently on CPU and is several hundred times faster than state-of-the-art predictive models, while performing as well.