Heating energy demand estimation of the EU building stock: Combining building physics and artificial neural networks

The aim of this study is to present a novel data-driven approach developed for space heating energy demand calculation of the whole EU building stock. To develop a computationally efficient bottom-up model that takes into account building physics parameters and details of the building stock make-up, an artificial neural network (ANN) is trained on a dataset of precise building-physics models. For this purpose, 2025 building energy simulations were performed in this study, ensuring representativeness for the entire EU, in terms of building stock properties (3 building geometries and 27 combinations of building envelope properties) and climatic conditions (25 weather files). The developed model is the first comprehensive hybrid (ANN and building physics) approach based on a condensed selection of input features, yet covering a wide range of climatic and building stock values, hence suitable for consideration of the spatial, temporal and multinational scale of the EU building stock. A unique building stock model was developed for this study at the EU regional level. The performance of the ANNs was validated by assessing the RMSE and R2 against the training (85%), testing (15%) and total dataset (100%, i. e. 2025 data points), to ensure adequate robustness, accuracy and generalisation capacity. As to the application of the ANN model on the building stock of 27 EU countries, a good accordance between ANN model results and measured consumption over the nine years from 2011 to 2019, was obtained, with an average overestimation by 9.3% (standard deviation (SD) = 5%), which confirmed the adequacy of the adopted methodology. Additional consideration of the occupant behaviour resulted in average overestimating the actual measurements by only 2.4% (SD = 4.7%). In addition to the EU and country level, the added value of the implemented bottom-up approach is its ability to assess the heating energy demand at regional level. Overall, the presented approach can be considered accurate and reliable to be used in future research on the long-term impact of building renovation with respect to energy savings and reductions of greenhouse gas emissions.