The study monitored 10 real European buildings to identify comfort enhancement potential and energy saving potential based on changing the behavior of building users. The results show that the office building could have saved up to EUR 2500 in energy during the study period while the healthcare centers could have saved more than EUR 1000. The highest energy saving potential is related to the use of the Heating, Ventilation and Air-Conditioning (HVAC) system. It has also been found out that there is a potential to improve comfort conditions in all the pilot buildings. The highest potential on comfort is identified in the improvement of the luminance conditions.

Non-Intrusive Load Monitoring (NILM) represents a powerful approach for energy disaggregation, which enables detailed insights into energy consumption patterns without requiring extensive sensor deployment. While significant advances have been achieved in residential NILM applications, commercial and industrial buildings remain largely underexplored despite their substantial contribution to global energy consumption. This study addresses this gap by developing and evaluating multiple artificial intelligence approaches for energy disaggregation across residential, commercial, and industrial buildings under a unified experimental protocol. We implement and compare several AI-based models, including Vision Transformer (ViT), Variational Autoencoder (VAE), Random Forest (RF), and custom architectures inspired by TimeGPT and Prophet, alongside traditional baseline methods. The proposed framework is validated using three benchmark datasets representing residential (AMPds), commercial (COmBED), and industrial (IMDELD) environments. Experimental results demonstrate that architecture–load interactions, rather than model complexity alone, are the primary determinants of disaggregation accuracy: the ViT-small configuration achieves superior performance for complex industrial loads with R² values exceeding 0.94, Random Forest proves most effective for finite-state commercial HVAC systems with R² up to 0.97, and the Prophet-inspired model excels in capturing seasonal patterns in residential appliances. These findings provide evidence-based guidelines for selecting appropriate AI models based on load characteristics, signal-to-noise ratio, and building type, contributing to the practical deployment of NILM in heterogeneous building environments.

Due to end-users’ habit being one of the prominent factors influencing building performance, changing user behavior to encourage energy efficient habits has tremendous potential to facilitate energy savings. Four ongoing European projects, eTeacher, FEEdBACk, InBetween, and UtilitEE, developed ICT solutions achieve this change through triggering mechanisms, informative feedback, gamification, and automation services within a human-centric and context aware framework. This paper provides each project’s methodology to enhance end-user engagement of their respective approach, methodology to assess the expected results, technical and legal challenges, and the preliminary results of their ICT solutions, which have also been presented at Sustainable Places 2020 conference.