Experimental application of a semi-parametric model for interpretable and accurate egression analysis of building energy consumption Eiraudo, Simone Gijón, Alfonso Manjavacas, Antonio Schiera, Daniele Salvatore Barbierato, Luca Molina Solana, Miguel José Gómez Romero, Juan Giannantonio, Roberta Bottaccioli, Lorenzo Lanzini, Andrea Semi-parametric models Regression analysis Hybrid models Regression analysis is a versatile tool with numerous applications across diverse domains. Its utility extends to several tasks, including forecasting, inverse modeling, anomaly detection, and pattern identification. Over the years, researchers have mainly focused on two regression categories: parametric and non-parametric analysis. In light of the benefits and drawbacks of both methods, this work introduces a semi-parametric approach, combining regression accuracy and interpretability. This is achieved by designing a hybrid model, that includes a physics-based sub-model and a neural network. The proposed data-driven pipeline is applied to a relevant case study from the energy sector, namely the analysis of building energy consumption, achieving high accuracy compared to the parametric approach. Results demonstrate an increase in the mean coefficient of determination, from 0.77 to 0.94, with a MAPE drop from 5.5 % to 2.2 %. Meanwhile, the semi-parametric model allows the assessment of the thermal behavior of the buildings, thereby offering an improvement over black-box approaches. 2025-10-27T09:08:15Z 2025-10-27T09:08:15Z 2025-12-01 journal article Eiraudo, S., Gijón, A., Manjavacas, A., Schiera, D. S., Barbierato, L., Molina-Solana, M., Gómez-Romero, J., Giannantonio, R., Bottaccioli, L., & Lanzini, A. (2025). Experimental application of a semi-parametric model for interpretable and accurate egression analysis of building energy consumption. Energy and Buildings, 349(116495), 116495. https://doi.org/10.1016/j.enbuild.2025.116495 https://hdl.handle.net/10481/107457 10.1016/j.enbuild.2025.116495 eng info:eu-repo/grantAgreement/EU/PRTR/MIA.2021.M04.0008 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier