标题： 短期二氧化碳排放预测：来自意大利电力市场的见解 显示英文标题

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

摘要： 本研究探讨了意大利电力市场发电碳排放的短期预测。 使用2021年至2023年的每小时数据，在国家和区域层面评估和比较了多种统计模型和预测组合方法。 考虑了四类主要模型：(i) 线性参数模型，如季节性自回归积分滑动平均及其外生变量扩展；(ii) 函数参数模型，包括有和没有外生变量的季节性函数自回归模型；(iii) (半)非参数且可能非线性模型，特别是广义可加模型（GAM）和TBATS（三角季节性、Box-Cox变换、ARMA误差、趋势和季节性）；以及(iv) 基于K近邻的半函数方法。 还考虑了预测组合，包括简单平均、最优Bates和Granger加权方案，以及一种基于选择的策略，该策略为每个小时选择最佳模型。 总体结果表明，GAM在白天时段报告了最准确的预测，而函数参数模型在清晨时段表现最好。 根据每小时平均均方根误差和Diebold-Mariano（DM）检验，GAM也可以被视为最佳单一模型。 在组合方法中，基于选择的方法始终优于所有单一模型和预测组合，与单一模型相比，均方根误差显著减少，并且是DM检验的主要选择。 这些发现强调了混合预测框架在提高电力系统短期碳排放预测准确性与可靠性方面的价值。 显示英文摘要

摘要： 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 exogenousvariable 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 systems.