A framework to predict the price of energy for the end-users with applications to monetary and energy policies
Stefanos G. Baratsas,
Alexander M. Niziolek,
Onur Onel,
Logan R. Matthews,
Christodoulos A. Floudas,
Detlef R. Hallermann,
Sorin M. Sorescu and
Efstratios N. Pistikopoulos ()
Additional contact information
Stefanos G. Baratsas: Texas A&M University
Alexander M. Niziolek: Texas A&M University
Onur Onel: Texas A&M University
Logan R. Matthews: Texas A&M University
Christodoulos A. Floudas: Texas A&M University
Detlef R. Hallermann: Texas A&M University
Sorin M. Sorescu: Texas A&M University
Efstratios N. Pistikopoulos: Texas A&M University
Nature Communications, 2021, vol. 12, issue 1, 1-12
Abstract:
Abstract Energy affects every single individual and entity in the world. Therefore, it is crucial to precisely quantify the “price of energy” and study how it evolves through time, through major political and social events, and through changes in energy and monetary policies. Here, we develop a predictive framework, an index to calculate the average price of energy in the United States. The complex energy landscape is thoroughly analysed to accurately determine the two key factors of this framework: the total demand of the energy products directed to the end-use sectors, and the corresponding price of each product. A rolling horizon predictive methodology is introduced to estimate future energy demands, with excellent predictive capability, shown over a period of 174 months. The effectiveness of the framework is demonstrated by addressing two policy questions of significant public interest.
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
https://www.nature.com/articles/s41467-020-20203-2 Abstract (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:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20203-2
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-020-20203-2
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().