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A framework to predict the price of energy for the end-users with applications to monetary and energy policies

Author

Listed:
  • Stefanos G. Baratsas

    (Texas A&M University
    Texas A&M University)

  • Alexander M. Niziolek

    (Texas A&M University
    Texas A&M University)

  • Onur Onel

    (Texas A&M University
    Texas A&M University)

  • Logan R. Matthews

    (Texas A&M University
    Texas A&M University)

  • Christodoulos A. Floudas

    (Texas A&M University
    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
    Texas A&M University)

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.

Suggested Citation

  • Stefanos G. Baratsas & Alexander M. Niziolek & Onur Onel & Logan R. Matthews & Christodoulos A. Floudas & Detlef R. Hallermann & Sorin M. Sorescu & Efstratios N. Pistikopoulos, 2021. "A framework to predict the price of energy for the end-users with applications to monetary and energy policies," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20203-2
    DOI: 10.1038/s41467-020-20203-2
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    Cited by:

    1. Baratsas, Stefanos G. & Niziolek, Alexander M. & Onel, Onur & Matthews, Logan R. & Floudas, Christodoulos A. & Hallermann, Detlef R. & Sorescu, Sorin M. & Pistikopoulos, Efstratios N., 2022. "A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies," Applied Energy, Elsevier, vol. 305(C).
    2. Li, Tianxiao & Liu, Pei & Li, Zheng, 2021. "Optimal scale of natural gas reserves in China under increasing and fluctuating demand: A quantitative analysis," Energy Policy, Elsevier, vol. 152(C).

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