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While most existing research relies on point forecasts to predict CO<jats:sub>2<\/jats:sub> 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\u00a0gCO<jats:sub>2<\/jats:sub>\/kWh, the lowest mean absolute percentage error 8.17%, and the random forest has the lowest root mean square error with 57.61 gCO<jats:sub>2<\/jats:sub>\/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.<\/jats:p>","DOI":"10.1186\/s42162-024-00303-9","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T08:02:18Z","timestamp":1704873738000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Short-term forecasting of German generation-based CO2 emission factors using parametric and non-parametric time series models"],"prefix":"10.1186","volume":"7","author":[{"given":"Adrian","family":"Ostermann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arian","family":"Bajrami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Bogensperger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"303_CR1","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1080\/07474938.2010.481556","volume":"29","author":"NK Ahmed","year":"2010","unstructured":"Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. 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