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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)

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  • Jeong, Kwangbok
  • Koo, Choongwan
  • Hong, Taehoon

Abstract

Electricity consumption in educational facilities has increased by an annual average of 9.84% since 2006. However, it is not considered as a factor in determining the AECB (annual energy cost budget) in South Korea. Therefore, this study aims to develop an estimation model for determining the AECB in educational facilities using the SARIMA (seasonal autoregressive integrated moving average) model and the ANN (artificial neural network). This study collected electricity consumption data for 7 years (2005–2011) from 787 educational facilities. The result of this study showed that the prediction accuracy of the proposed hybrid model (which was developed by combining SARIMA and ANN) was improved, compared to the conventional SARIMA model. The MAPE (mean absolute percentage error) of the proposed method and conventional method for determining the AECB in educational facilities was determined at 0.11–0.24% and 1.23–1.84%, respectively. Namely, it was determined that the proposed method was superior to the conventional method. The proposed model could enable executives and managers in charge of budget planning to accurately determine the AECB in educational facilities. It could be also applied to other types of resources (e.g., water consumption or gas consumption) used in educational facilities.

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  • 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.
    • Handle: RePEc:eee:energy:v:71:y:2014:i:c:p:71-79
    DOI: 10.1016/j.energy.2014.04.027
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    17. Yaqi Wang & Renzhou Gui, 2022. "A Hybrid Model for GRU Ultra-Short-Term Wind Speed Prediction Based on Tsfresh and Sparse PCA," Energies, MDPI, vol. 15(20), pages 1-20, October.
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    22. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
    23. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
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