Modelling and forecasting daily electricity load curves: a hybrid approach

TL;DR

This paper introduces a hybrid model combining trend-seasonality fitting with a novel curve linear regression technique to improve short-term electricity load forecasting.

Contribution

It presents a new methodology for curve linear regression using Hilbert space SVD, enhancing load forecasting accuracy.

Findings

The hybrid model outperforms existing models on French load data.

The SVD-based curve regression effectively captures dependence across daily loads.

The approach improves short-term load forecasting accuracy.

Abstract

We propose a hybrid approach for the modelling and the short-term forecasting of electricity loads. Two building blocks of our approach are (i) modelling the overall trend and seasonality by fitting a generalised additive model to the weekly averages of the load, and (ii) modelling the dependence structure across consecutive daily loads via curve linear regression. For the latter, a new methodology is proposed for linear regression with both curve response and curve regressors. The key idea behind the proposed methodology is the dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several ordinary (i.e. scalar) linear regression problems. We illustrate the hybrid method using the French electricity loads between 1996 and 2009, on which we also compare our method with other available models including the EDF operational model.