Methodological Advances in temperature dynamics modeling for energy-efficient indoor air management systems

Abstract

Heating, ventilation, and air conditioning (HVAC) systems account for up to 40% of the total energy consumption in buildings. Improving the modeling of HVAC components is necessary to optimize energy efficiency, maintain indoor thermal comfort, and reduce their carbon footprint. This work addresses the lack of a general methodology for data preprocessing by introducing a novel approach for feature extraction and feature selection based on physical equations and expert knowledge that can be applied to any data-driven model. The proposed framework enables the forecasting of indoor temperatures and the energy consumption of individual HVAC components. The methodology is validated with real-world data from a system involving a fan coil unit and a thermal inertia deposit powered by geothermal energy, achieving a coefficient of determination (R2) of 0.98 and mean absolute percentage error (MAPE) of 0.44%

This project was undertaken by the eXiT research group (SITES group, Ref. 2021 SGR 01125) under a grant from the Generalitat de Catalunya. The research received funding from the European Union NextGenerationEU/PRTR under OptiREC project grant agreement TED2021-131365B-C41 and the GERIO project under grant agreement PID2022-142221OB-I00; from the (Departament de Recerca i Universitats, del Departament d’Acció Climàtica, Alimentació i Agenda Rural i del Fons Climàtic de la Generalitat de Catalunya) under CLIMA project grant agreement No 2023 CLIMA 00090; and the ACCIO of Generalitat de Catalunya under AI ENERGY project grant agreement nuclis T083-24