A SARIMAX coupled modelling applied to individual load curves intraday forecasting

TL;DR

This paper introduces a dynamic coupled SARIMAX-based model that incorporates temperature as an exogenous factor to improve intraday individual energy load forecasting, offering a simpler yet effective alternative to complex models.

Contribution

It proposes a novel dynamic coupled modeling approach integrating temperature for individual load forecasting, balancing model simplicity and predictive accuracy.

Findings

Model effectively captures temperature influence on energy load.

The approach provides accurate intraday forecasts on real data.

It offers a promising alternative to traditional complex models.

Abstract

A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARIMAX models and to take advantage of the usual linear relation between energy consumption and temperature for thermosensitive customers. We first recall some issues related to individual load curves forecasting. Then, we propose and study the properties of a dynamic coupled modelling taking temperature into account as an exogenous contribution and its application to the intraday prediction of energy consumption. Finally, these theoretical results are illustrated on a real individual load curve. The authors discuss the relevance of such an approach and anticipate that it could form a substantial alternative to the commonly used methods for energy consumption forecasting of individual customers.