Developing and Validating Heating Energy Consumption Models for Schools in Osijek-Baranja County, Croatia

This study develops and validates heating energy consumption models for school buildings in Osijek-Baranja County in Croatia, integrating an overview of the legal framework, a review of heating energy consumption in schools, and approaches to predicting consumption. Three models, Multiple linear regression (MLR), Artificial neural networks (ANN), and Random forest (RF), were tested using training and validation datasets. Although ANN and RF achieved higher accuracy during training, their complexity and computational demands reduce their suitability for everyday use in schools. MLR, despite slightly lower accuracy (R2 = 0.897 on validation), proved to be the most practical due to its simplicity, transparency, and minimal resource requirements. Additional testing on schools in eastern Croatia confirmed its strong performance, with high R2 and low MAPE values, reflecting the uniformity of heating systems that predominantly rely on gas or district heating. In contrast, prediction accuracy decreases in coastal regions where diverse fuels such as electricity or heating oil are used, indicating the need for region-specific models. Overall, the findings show that MLR is the most applicable model for widespread implementation, while ANN and RF offer potential for specialized cases or future enhancement.