Work

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

Year: 2024

Type: article

Abstract: 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 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 of 0950. On the validation set, the ANN model maintained an R² 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.

Source: Energy Reports

DOI: 10.1016/j.egyr.2024.09.039

Authors: Hana Begić Juričić, Hrvoje Krstić

Institution: University of Osijek

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,700