Predicting US Energy Consumption Utilizing Artificial Neural Network

Mohammad Pasandidehpoor (), João Mendes-Moreira (), Soulmaz Rahman Mohammadpour and Ricardo Teixeira Sousa ()
Additional contact information
Mohammad Pasandidehpoor: University of Porto
João Mendes-Moreira: University of Porto
Soulmaz Rahman Mohammadpour: Islamic Azad University
Ricardo Teixeira Sousa: University of Porto

A chapter in Handbook of Smart Energy Systems, 2023, pp 2075-2087 from Springer

Abstract: 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%.

Keywords: Energy consumption; Prediction; Artificial neural network; Global climate change; Machine learning (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-030-97940-9_136

Ordering information: This item can be ordered from
http://www.springer.com/9783030979409

DOI: 10.1007/978-3-030-97940-9_136

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().