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Predicting US Energy Consumption Utilizing Artificial Neural Network

In: Handbook of Smart Energy Systems

Author

Listed:
  • Mohammad Pasandidehpoor

    (University of Porto)

  • João Mendes-Moreira

    (University of Porto)

  • Soulmaz Rahman Mohammadpour

    (Islamic Azad University)

  • Ricardo Teixeira Sousa

    (University of Porto)

Abstract

Today, the increasing importance of energy resources in the formation and growth of economic processes, as well as the necessity of utilizing these resources based on environmental considerations and sustainable economic and social development, highlights the issue of identifying and studying the factors affecting energy consumption. In addition, due to the limitation of energy resources, forecasting the demand for energy consumption in the future has become very important. Therefore, this chapter proposed a smart prediction methodology to predict the US energy consumption via using machine learning (ML) methodologies. It proposes artificial neural networks (ANN) to predict energy consumption in the United States from 2021 to 2030. In addition, it investigates a vast range of influencers on energy consumption and selects four influencers on energy consumption during the years from 1990 until 2020 including population, gross domestic product (GDP), crude oil production, and inflation rate. Finally, this research presents the volume of future US energy consumption at a high level of accuracy which maximum value of ANN-PB algorithm error equaled to 2.5%.

Suggested Citation

  • Mohammad Pasandidehpoor & João Mendes-Moreira & Soulmaz Rahman Mohammadpour & Ricardo Teixeira Sousa, 2023. "Predicting US Energy Consumption Utilizing Artificial Neural Network," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 2075-2087, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_136
    DOI: 10.1007/978-3-030-97940-9_136
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