Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230238
Authors: 
Kraft, Emil
Keles, Dogan
Fichtner, Wolf
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] [Issue:] [Pages:] n/a-n/a
Abstract: 
The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from econometrics and artificial intelligence and set up a forecasting framework based on autoregressive and exogenous factors. We use SARIMAX models as well as neural networks with different structures and forecast based on a rolling one‐step forecast with reestimation of the models. It turns out that the naive forecast performs reasonably well but is outperformed by the more advanced models. In addition, neural network approaches outperform the econometric approach in terms of forecast quality, whereas for the further use of the generated models the econometric approach has advantages in terms of explaining price drivers. For the present application, more advanced configurations of the neural networks are not able to further improve the forecasting performance.
Subjects: 
artificial neural network
balancing reserve
econometrics
electricity price
time series forecasting
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by-nc/4.0/
Document Type: 
Article
Document Version: 
Published Version

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