Rule-based Autoregressive Moving Average Models for Forecasting Load on Special Days: A Case Study for France

TL;DR

This study develops a rule-based SARMA model tailored for short-term load forecasting on special days in France, demonstrating superior accuracy over benchmarks using nine years of detailed load data.

Contribution

It introduces a French-specific rule-based adaptation of SARMA models for load forecasting on special days, improving accuracy over existing methods.

Findings

Rule-based SARMA outperforms benchmarks in forecast accuracy.

French-specific adaptations are necessary for effective modeling.

Model performs well across various lead times from half-hour to one day.

Abstract

This paper presents a case study on short-term load forecasting for France, with emphasis on special days, such as public holidays. We investigate the generalisability to French data of a recently proposed approach, which generates forecasts for normal and special days in a coherent and unified framework, by incorporating subjective judgment in univariate statistical models using a rule-based methodology. The intraday, intraweek, and intrayear seasonality in load are accommodated using a rule-based triple seasonal adaptation of a seasonal autoregressive moving average (SARMA) model. We find that, for application to French load, the method requires an important adaption. We also adapt a recently proposed SARMA model that accommodates special day effects on an hourly basis using indicator variables. Using a rule formulated specifically for the French load, we compare the SARMA models with a range of different benchmark methods based on an evaluation of their point and density forecast accuracy. As sophisticated benchmarks, we employ the rule-based triple seasonal adaptations of Holt-Winters-Taylor (HWT) exponential smoothing and artificial neural networks (ANNs). We use nine years of half-hourly French load data, and consider lead times ranging from one half-hour up to a day ahead. The rule-based SARMA approach generated the most accurate forecasts.