Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States

Arash Kialashaki and John R. Reisel

Energy, 2014, vol. 76, issue C, 749-760

Abstract: In the United States, the industrial sector is the driving engine of economic development, and energy consumption in this sector may be considered as the fuel for this engine. In order to keep this sector sustainable (diverse and productive over the time), energy planning should be carried out comprehensively and precisely. This paper describes the development of two types of numerical energy models which are able to predict the United States' future industrial energy-demand. One model uses an ANN (artificial neural network) technique, and the other model uses a MLR (multiple linear regression) technique. Various independent variables (GDP, price of energy carriers) are tested. The future industrial energy demand can then be forecasted based on a defined scenario.

Keywords: Energy demand forecast; Industrial sector; Artificial neural networks; Linear regression; Energy modeling; Energy price (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:76:y:2014:i:c:p:749-760

DOI: 10.1016/j.energy.2014.08.072

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