An Economic Analysis of Energy Consumption at Student Residences in a South African-Based Academic Institution Using NARX Neural Network

Olusola Olaitan Ayeleru (), Joshua Adeniyi Adeniran, Sula Bantubakhona Kwesi Ntsaluba, Lanrewaju Ibrahim Fajimi and Peter Apata Olubambi
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Olusola Olaitan Ayeleru: Centre for Nanoengineering and Tribocorrosion, University of Johannesburg, Johannesburg 2028, South Africa
Joshua Adeniyi Adeniran: Centre for Nanoengineering and Tribocorrosion, University of Johannesburg, Johannesburg 2028, South Africa
Sula Bantubakhona Kwesi Ntsaluba: Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2028, South Africa
Lanrewaju Ibrahim Fajimi: Centre for Nanoengineering and Tribocorrosion, University of Johannesburg, Johannesburg 2028, South Africa
Peter Apata Olubambi: Centre for Nanoengineering and Tribocorrosion, University of Johannesburg, Johannesburg 2028, South Africa

Energies, 2023, vol. 16, issue 2, 1-14

Abstract: One of the issues associated with the supply of electricity is its generation capacity, and this has led to prevalent power cuts and high costs of usage experienced in many developing nations, including South Africa. Historical research has shown that the annual rate of increase for electricity has grown at an alarming rate since 2008 and, in some years, has grown as much as 16%. The objectives of this study are to estimate the cost analysis of electricity usage at the twenty-nine residences of the University of Johannesburg (UJ-Res) and propose a model for our university, as well as other South African universities, to become more energy-efficient. This was achieved by analyzing the tariffs between 2015 and 2021. A forecast was made for a period of five years (2021 to 2026) using a non-linear autoregressive exogenous neural network ( NARX-NN ) time-series model. From the results obtained, the better NARX-NN model studied has a root mean squared error ( RMSE ) of 2.47 × 10 5 and a determination coefficient ( R 2 ) of 0.9661. The projection result also shows that the annual cost of energy consumed will increase for the projected years, with the year 2022 being the peak with an estimated annual cost of over ZAR 30 million (USD 2,076,268).

Keywords: economic analysis; energy consumption; deep learning; NARX-NN; University of Johannesburg Residences (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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