Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case

Emma Viviani, Luca Di Persio and Matthias Ehrhardt
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Emma Viviani: Department of Computer Science, College of Mathematics, University of Verona, 37134 Verona, Italy
Luca Di Persio: Department of Computer Science, College of Mathematics, University of Verona, 37134 Verona, Italy
Matthias Ehrhardt: Department of Applied Mathematics and Numerical Analysis, University of Wuppertal, 42119 Wuppertal, Germany

Energies, 2021, vol. 14, issue 2, 1-33

Abstract: In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.

Keywords: electricity price; statistical method; autoregressive; probabilistic forecast; neural network (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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