Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices

Stefano Frizzo Stefenon (), Laio Oriel Seman, Viviana Cocco Mariani and Leandro dos Santos Coelho
Additional contact information
Stefano Frizzo Stefenon: Digital Industry Center, Fondazione Bruno Kessler, 38123 Trento, Italy
Laio Oriel Seman: Graduate Program in Applied Computer Science, University of Vale do Itajai, Itajai 88302-901, Brazil
Viviana Cocco Mariani: Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil
Leandro dos Santos Coelho: Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil

Energies, 2023, vol. 16, issue 3, 1-18

Abstract: The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years; however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoothing) and Facebook Prophet methodologies to perform a more accurate and resilient time series analysis of Italian electricity spot prices. This can assist in enhancing projections and better understanding the variables driving the data, while also including additional information such as holidays and special events. The combination of approaches improves forecast accuracy while lowering the mean absolute percentage error (MAPE) performance metric by 18% compared to the baseline model.

Keywords: electricity spot prices; electrical power systems; time series decomposition; time series forecasting (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 (11)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1371/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1371/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1371-:d:1049947

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().