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

Laura Böhm, Sebastian Kolb (), Thomas Plankenbühler, Jonas Miederer, Simon Markthaler and Jürgen Karl
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Laura Böhm: Chair of Energy Process Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fürther Straße 244f, 90429 Nürnberg, Germany
Sebastian Kolb: Chair of Energy Process Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fürther Straße 244f, 90429 Nürnberg, Germany
Thomas Plankenbühler: Chair of Energy Process Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fürther Straße 244f, 90429 Nürnberg, Germany
Jonas Miederer: Chair of Energy Process Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fürther Straße 244f, 90429 Nürnberg, Germany
Simon Markthaler: Chair of Energy Process Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fürther Straße 244f, 90429 Nürnberg, Germany
Jürgen Karl: Chair of Energy Process Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fürther Straße 244f, 90429 Nürnberg, Germany

Energies, 2023, vol. 16, issue 18, 1-25

Abstract: 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/t CO2 (carbon prices) corresponding to a normalized RMSE of 0.023.

Keywords: neural network; natural gas price; carbon price; multi-layer perceptron; price prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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