Energy Saving Potentials of a Centralized Hybrid Heating System via Adaptive Model Predictive Control in a Northern Italy Residential Building

Extensive research has been conducted on advanced control techniques for buildings in recent years. However, even though in theory and in few experimental studies, the benefit of advanced Building Energy Management Systems was shown in most newly built or renovated buildings a traditional approach is still preferred, due to the higher cost and complexity of more advanced approaches despite the benefits. This paper presents a Python-Modelica grey-box modelling and adaptive optimal control of a hybrid heating system coupled with a thermal storage, that aims to reduce costs and improve the performance by better exploiting the hybrid heating system and the thermal storage, within the project TEPORE. The results show that for the chosen case study there are not enough degrees of freedom for the hybrid heating system to be optimized However, interesting results emerged from changing the heating capacity of the generators showing on average 50% reduction in running costs.