Short-term CO2 emissions forecasting: insight from the Italian electricity market

TL;DR

This paper evaluates various statistical and hybrid models for short-term CO2 emissions forecasting in the Italian electricity market, emphasizing the importance of zonal differences and model combinations for improved accuracy.

Contribution

It introduces a comprehensive comparison of multiple modeling approaches and forecast combination strategies tailored to the Italian power market's emissions data.

Findings

GAM performs best during daytime hours

Functional parametric models excel in early mornings

Simple averaging and selection-based combinations outperform individual models

Abstract

This study investigates the short-term forecasting of carbon emissions from electricity generation in the Italian power market. Using hourly data from 2021 to 2023, several statistical models and forecast combination methods are evaluated and compared at the national and zonal levels. Four main model classes are considered: (i) linear parametric models, such as seasonal autoregressive integrated moving average and its exogenous variable extension; (ii) functional parametric models, including seasonal functional autoregressive models, with and without exogenous variables; (iii) (semi) non-parametric and possibly non-linear models, notably the generalised additive model (GAM) and TBATS (trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonality); and (iv) a semi-functional approach based on the K-nearest neighbours. Forecast combinations include simple averaging, the optimal Bates and Granger weighting scheme, and a selection-based strategy that chooses the best model for each hour. The results show that the GAM produces the most accurate forecasts during the daytime hours, while the functional parametric models perform best in the early morning. Among the combination methods, the simple average and the selection-based approaches consistently outperform all individual models. The findings underscore the value of hybrid forecasting frameworks in improving the accuracy and reliability of short-term carbon emissions predictions in power systems. In addition, they highlight the importance of considering zonal specificities when implementing flexible energy demand strategies, as the timing of low-carbon emissions varies between market zones throughout the day.