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The development of a reliable energy use prediction model is still difficult due to the inherent complex pattern of energy use data. There are few studies developing a prediction model for the one-day-ahead energy use prediction in buildings and optimizing the hyperparameters of a prediction model is necessary. This study aimed to propose a hybrid artificial intelligence model for forecasting one-day ahead time-series energy consumption in buildings. The proposed model was developed based on the integration of the Seasonal Autoregressive integrated Moving average, the Firefly-inspired Optimization algorithm, and the support vector Regression (SAMFOR). A large dataset of energy consumption in 30-min intervals, temporal data, and weather data from six real-world buildings in Vietnam was used to train and test the model. Sensitivity analyses were performed to identify appropriate model inputs. Comparison …