Energy predicting gains attention for its ability to manage and control energy consumption in a building. The multiple linear regression model is known for its simplicity and effective when dealing with electricity consumption. In this work, the authors have utilized the Multiple Linear Regression (MLR) model to predict the hourly electricity energy consumption, in winter, for school buildings. For the case study, school buildings in the South of France are used. In this model, nine predictor variables are considered, namely, (1) level of indoor CO2, (2) indoor temperature, (3) indoor humidity, (4) outdoor temperature, (5) outdoor humidity, (6) global solar radiation, (7) day index (weekday/weekend), ( 8) time index (occupied/non-occupied) and ( 9) building net floor area. The first order and two-way interaction models are constructed using all predictors. The coefficient of determination (R 2 ) is a model evaluation metric that assesses the relationship between the vales of the desired outcomes and those that the model predicts. The results show that the two-way interaction model has better R 2 for both training set (R 2 = 74%) and testing set (R 2 = 77%). However, this model gives underestimated results for higher values of electricity consumption starting from 30kWh/hour. It is also not reliable for one of the buildings as the R 2 is only 55% and the inaccuracy rate is 69%. Overall, this model is a starting point for future work to improve its predicting ability by adding other influential explanatory variables.