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

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 are also considered including simple averaging, the optimal Bates and Granger weighting scheme, and a selection-based strategy that chooses the best model for each hour. The overall findings indicate that GAM reports the most accurate forecasts during the daytime hours, while functional parametric models perform best during the early morning period. GAM can also be considered the best individual model according to the hourly average root mean square error and the Diebold-Mariano (DM) test. Among the combination methods, the selection-based approach consistently outperforms all individual models and forecast combinations, resulting in a substantial reduction in the root mean square error compared to single models and a primary choice for the DM test. These findings underline the value of hybrid forecasting frameworks in improving the accuracy and reliability of short-term carbon emissions predictions in power system.