Short-term forecasting of German generation-based CO2 emission factors using parametric and non-parametric time series models

Abstract

This study focuses on forecasting German generation-based CO2 emission factors to develop accurate prediction models, which help to shift flexible loads in time with low emissions. While most existing research relies on point forecasts to predict CO2 emission factors, the presented methods are utilized to perform interval forecasts. In addition, compared to other studies, recent data that extends over a long period is used. The study describes the used data and discusses the concept of walk-forward validation. Further, various models are employed and tuned to forecast the emission factors, including benchmark, parametric (e.g., SARIMAX), and non-parametric (bagging, random forest, gradient boosting, CNN, LSTM, MLP) models. The study reveals that all applied parametric and non-parametric models yield better results than the benchmark models, while the gradient boosting model has the lowest mean absolute error with 40.66 gCO2/kWh, the lowest mean absolute percentage error 8.17%, and the random forest has the lowest root mean square error with 57.61 gCO2/kWh. However, the potential of the deep learning models was not fully exploited. In a live application, the implementation effort should be evaluated against the benefit of better prediction.