Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/281714 
Year of Publication: 
2023
Citation: 
[Journal:] Amfiteatru Economic Journal [ISSN:] 2247-9104 [Volume:] 25 [Issue:] 63 [Year:] 2023 [Pages:] 463-478
Publisher: 
The Bucharest University of Economic Studies, Bucharest
Abstract: 
Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have been developed and proven to work in the area of EPF. This paper proposes a new univariate hybrid model, trained, and tested on German electricity market data, based on the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the NeuroFuzzy-Local Linear Wavelet Neural Network (LLWNN). Although a series of complex challenges create difficulties in refining the model, the proposed algorithm significantly narrows the gap between predictions and actual prices. The ability to predict the dynamics of the price of electricity on the spot market is an important asset for both suppliers and consumers, with a view on prophylactic calibration of supply-demand ratios. The model can be extended and applied to any energy market with a stable structure.
Subjects: 
electricity price forecasting
Seasonal Auto-Regressive Integrated Moving Average (SARIMA)
NeuroFuzzy-Local Linear Wavelet Neural Network (LLWNN)
univariate hybrid model
German electricity market.
JEL: 
Q47
C51
C52
C53
C55
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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