Throughout its duration, the PRECEPT project executed a range of activities that laid the groundwork for a transition in residential building management. Starting with the development of a global IoT framework, the project integrated advanced algorithms that enabled not only predictive maintenance but also adaptive responses to changing environmental conditions and resident behaviours. This framework was pivotal in achieving significant reductions in energy use and operational costs, which were further bolstered by the project's advanced prescriptive maintenance strategies. Additionally, the project's innovations in occupancy and activity inference utilised machine learning to refine energy management, optimising HVAC systems to balance energy consumption with optimal user comfort efficiently. These efforts were supported by real-time assessments of thermal and visual comfort, ensuring that adjustments to the living environment were both responsive and effective. The KPIs highlight these achievements: maintenance costs were reduced by 49%, operational costs by 15%, and energy savings from the HVAC control alone saved 21%. Furthermore, improvements in energy efficiency and indoor environmental quality were marked at 19%, with a reduction in peak energy use by 16%. The practical applications of PRECEPT's technologies were thoroughly tested in pilot programs involving residents across multiple countries. These pilots were instrumental in refining the technologies and gathering user feedback, which was overwhelmingly positive, with user acceptance and satisfaction rates exceeding 70%. The dissemination of these results through various channels ensured that the lessons learned and best practices from PRECEPT reached a broad audience, facilitating wider adoption and impact.
