Comparative study on energy consumption at the University of Johannesburg residences

Ayeleru, Olusola Olaitan; Adeniran, Joshua Adeniyi; Ntsaluba, Sula B.K.; Koker, J. J. de

doi:10.23919/due.2017.7931826

2017 International Conference on the Domestic Use of Energy (DUE) 2017

DOI: 10.23919/due.2017.7931826

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Comparative study on energy consumption at the University of Johannesburg residences

Olusola Olaitan Ayeleru

Joshua Adeniyi Adeniran

Sula B.K. Ntsaluba

et al.

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Cited by 3 publications

(2 citation statements)

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“…Value of the Data The real-time data provided in this article will aid the development of mathematical models suitable for a student residence electricity consumption profile [1] . The real-time data provided in this article is useful in the development of energy management strategies and for the design, sizing, modelling, and simulation of alternative energy systems for a student residence [ 2 , 3 ].…”

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Electricity consumption data of a student residence in Southern Africa

et al. 2020

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The time-series dataset presented in this article was captured using a real-time energy monitoring device from a distribution panel of a student residence in Johannesburg, South Africa. The data was captured from April 2016 to January 2018. The data from the three conductors supplying the student residence with electricity was automatically aggregated and presented as a single data point. The granularity was at resolution levels of watt-minute and kilowatt-hour. A total of 13,966 hrs of data points was captured. The data has not been processed further. Hence, data consists of 1,209-hour of missing data points. In addition to the energy consumption data, 16 months of hourly data for wind speed, temperature and humidity of the closest weather station has been provided. The data will be useful in the formulation of mathematical models of electricity consumption that is most suitable for a student residence. Furthermore, the data provided in this article will encourage the development of a data-driven electricity consumption management strategy and policy formulation for student residences.

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confidence: 99%

“…The real-time data provided in this article will aid the development of mathematical models suitable for a student residence electricity consumption profile [1] .…”

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confidence: 99%

Electricity consumption data of a student residence in Southern Africa

et al. 2020

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Quantifying the Impact of Carbon Reduction Interventions and Incentive Mechanisms in Campus Buildings: A Case Study from a Chinese University

Xue,

Xu,

Zhang

et al. 2024

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With the development of sustainable cities, densely populated higher education institutions increasingly emphasize the sustainability of campuses and their impact on the environment. However, there is a lack of means to quantify emission reduction measures. This study aims to propose an evaluation framework that can quantify energy conservation and emission reduction measures and incentive policies. To this end, this study adopts a mixed methods approach, using questionnaires to assess the effectiveness of management and communication interventions and the impact of incentives on residents’ willingness to participate in emission reduction efforts. The survey results show that although the support for the intervention measures is slightly higher than the average, specific measures such as adjusting dormitory lights-out time and providing sports equipment show superior emission reduction potential. Universities could reduce carbon emissions by about 560 tons per year without incentives and just using interventions. However, when incentives and interventions are combined, the university’s annual emissions reductions are expected to increase to 800 to 1045 tons. Research also highlights the importance of understanding the relationship between occupant behavior, energy consumption, and building carbon emissions. By quantifying the impact of carbon reduction measures and incentives on the daily behaviors of residents, universities can more effectively implement sustainable campus strategies.

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

et al. 2023

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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 × 105 and a determination coefficient (R2) 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).

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Comparative study on energy consumption at the University of Johannesburg residences

Cited by 3 publications

References 14 publications

Electricity consumption data of a student residence in Southern Africa

Electricity consumption data of a student residence in Southern Africa

Quantifying the Impact of Carbon Reduction Interventions and Incentive Mechanisms in Campus Buildings: A Case Study from a Chinese University

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

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