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School Electricity Consumption in a Small Island Country: The Case of Fiji

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  • Ravita D. Prasad

    (College of Engineering, and Technical Vocational Education and Training, Fiji National University, Samabula, Suva P.O. Box 3722, Fiji)

Abstract

Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand ( ED ) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R 2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R 2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies.

Suggested Citation

  • Ravita D. Prasad, 2024. "School Electricity Consumption in a Small Island Country: The Case of Fiji," Energies, MDPI, vol. 17(7), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1727-:d:1369855
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    References listed on IDEAS

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    4. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
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    1. Pruethsan Sutthichaimethee & Grzegorz Mentel & Volodymyr Voloshyn & Halyna Mishchuk & Yuriy Bilan, 2024. "Modeling the Efficiency of Resource Consumption Management in Construction Under Sustainability Policy: Enriching the DSEM-ARIMA Model," Sustainability, MDPI, vol. 16(24), pages 1-17, December.

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