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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 …