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Buildings must be energy efficient and sustainable because buildings have contributed significantly to world energy consumption and greenhouse gas emission. Predicting energy consumption patterns in buildings is beneficial to utility companies, users, and facility managers because it can help to improve energy efficiency. This work proposed a Random Forests (RF) – based prediction model to predict the short-term energy consumption in the hourly resolution in multiple buildings. Five one-year datasets of hourly building energy consumption were used to examine the effectiveness of the RF model throughout the training and test phases. The evaluation results presented that the RF model exhibited a good prediction accuracy in the prediction. In four evaluation scenarios, the mean absolute error (MAE) values ranged from 0.430 to 0.501 kWh for the 1-step-ahead prediction, from 0.612 to 0.940 kWh for the 12 …