Application of the Forecasting Technique by hybrid model for Forecasting the Electricity Demands of Rajabhat Universities


Thanakon Sutthison


     Electricity consumption at the education institutions studied is expected to increase which means that a more efficient and relevant model for managing electricity consumption is needed. To achieve this, statistical forecasting technique was applied. An accurate forecast of consumption would be great help for executives and for those concerning to establish a power-saving policy in the universities. The objective of this research was to forecast electricity consumption by using hybrid models (mixing the Box-Jenkins modeling and a support vector regression model). Three Rajabhat Universities: Nakorn Ratchatsima, Ubon Ratchathani and Loei, were included in the modeling. Time series data on the energy consumption on a monthly basis, covering the period from January, 2006 to September, 2017, were available on the website of the Energy Ministry. Data for the period of January, 2006 to December, 2016 were used for our research purposes, and R-language based forecast model that we developed was used for analysis. The most suitable model was selected by using the Mean Absolute Percent Error (MAPE). The most suitable model obtained from the data series from January to September, 2017 was used to measure the accuracy of the forecast produced. Our results indicated that the proposed model was more efficient, with greater accuracy, in forecasting the energy consumption than the conventional model currently used in the universities participating in the study. For these universities, the MAPE was 7.65428 for Nakorn Ratchatsima Rajabhat University, 6.35679 for Ubon Ratchathani and 4.13581 for Loei Rajabhat Universities.

Keywords: Electricity Consumption, Hybrid models, Box-Jenkins Model, Support Vector Regression Model



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Research Articles


How to Cite
SUTTHISON, Thanakon. Application of the Forecasting Technique by hybrid model for Forecasting the Electricity Demands of Rajabhat Universities. Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 27, n. 1, p. 18-31, mar. 2019. ISSN 2539-553X. Available at: <>. Date accessed: 16 apr. 2024. doi: