Comparing MLR and ANN models for school building electrical energy prediction in Osijek-Baranja County in Croatia

This paper presents a study conducted in Osijek-Baranja County, Croatia, to predict electrical energy consumption in school buildings. Data from the Energy Management Information System (EMIS) database for primary and secondary schools were analyzed using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). The ANN model achieved a high R2 2 of 0957 in the training set, with lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) than the MLR model, which had an R2 2 of 0950. On the validation set, the ANN model maintained an R2 2 of 0954 and showed slightly better performance with a lower Coefficient of Variation of RMSE (CVRMSE) of 19,79 %, compared to the MLR model's CVRMSE of 20,50 %. These results indicate that the ANN model generally provides more accurate and reliable predictions for energy consumption in school buildings. However, both models provided a robust positive correlation between the predicted and actual values.