Scenario-based Analysis for the Integration of Renewable Gases into the German Gas Market

Kolb, Sebastian; Plankembühler, T.; Pfaffenberger, Fabian; Vrzel, Janja; Kalu, Amarachi; Holtz‐Bacha, Christina; Ludwig, Ralf; Karl, Jürgen; Dillig, M.

doi:10.5071/27theubce2019-5bv.3.32

Cited by 1 publication

(1 citation statement)

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“…In this context, a multitude of modeling approaches for the prediction of energy prices exists, as Weron [3] highlights and classifies in his extensive review. Multi-agent [4][5][6][7][8][9] and fundamental [10][11][12] models replicate the existing plant fleet and market interactions in a high degree of detail and allow the analysis of long-term horizons. For shorter (e.g., day-to-day) predictions, statistical models (e.g., ARX-type [13][14][15][16], GARCH-type [17][18][19]) or computational intelligence models [20][21][22][23] are common.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks

Böhm,

Kolb,

Plankenbühler

et al. 2023

Energies

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Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023.

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