The strive for improved building energy performance has led to increased automation levels in building control systems, challenging the balance between energy efficiency and comfort. This often leads to dissatisfaction as occupants are unable to adjust conditions to their preferences, causing discomfort. Since people spend almost 90% of their time indoors, shifting to human-centered control (HCC) systems in buildings is crucial. HCC allows the integration of user preferences provided as self-reported subjective feedback into the building control system for indoor environmental control and grid flexibility actions. This thesis explores user-building interactions through data-driven personalized comfort models (PCMs) by implementing HCC in a building and focuses on the human aspect of HCC. Methods include a survey of nearly 1000 respondents, two field experiments, and simulations. Results from the field experiment show that integrating user preferences using PCMs increases comfort satisfaction by 17%. However, the survey shows that only 56% of respondents are willing to interact or share data with HCC, and this willingness can be predicted with 64% prediction potential using a Theory of Planned Behaviour framework. The results also suggest that integrating HCC with model predictive control (MPC) improves grid flexibility, achieving up to 45% energy savings, 61% load shifting and 59% cost savings compared to standard systems. These findings provide valuable insights for designing future HCC systems that prioritize occupant needs while empowering buildings in smart grids.