LAPSE:2023.11067
Published Article
LAPSE:2023.11067
Models of Electricity Price Forecasting: Bibliometric Research
February 27, 2023
Abstract
Electricity Price Forecasting (EPF) influences the sale conditions in the energy sector. Proper models of electricity price prognosis can be decisive for choice between energy sources as a start point of transformation toward renewable energy sources. This article aims to present and compare various EPF models scientific publications. Adopted in this study procedure, the EPF publications models are compared into two main categories: the most popular and the most accurate. The adopted method is a bibliometric study as a variation of Systematic Literature Review (SLR) with specified automated queries supported by the VOSviewer bibliometric maps exploration. The subject of this research is the exploration of EPF models in two databases, Web of Science and Scopus, and their content comparison. As a result, the SLR research queries were classified into two groups, the most cited and most accurate models. Queries characteristics were explained, along with the graphical presentation of the results. Future promising research avenues can be dedicated to the most accurate EPF model formulation proved by statistical testing of its significance and accuracy.
Keywords
electricity pricing, energy market, energy sector, forecasting models, VOSviewer
Suggested Citation
Zema T, Sulich A. Models of Electricity Price Forecasting: Bibliometric Research. (2023). LAPSE:2023.11067
Author Affiliations
Zema T: Department of Process Management, Faculty of Business Management, Wroclaw University of Economics and Business, ul. Komandorska 118/120, 53-345 Wroclaw, Poland [ORCID]
Sulich A: Department of Advanced Research in Management, Faculty of Business Management, Wroclaw University of Economics and Business, ul. Komandorska 118/120, 53-345 Wroclaw, Poland [ORCID]
Journal Name
Energies
Volume
15
Issue
15
First Page
5642
Year
2022
Publication Date
2022-08-03
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15155642, Publication Type: Journal Article
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LAPSE:2023.11067
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https://doi.org/10.3390/en15155642
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