A data-Driven approach for guiding the occupant’s actions to achieve better comfort in buildings

People spend the majority of their time in buildings which are a major contributor to overall energy consumption. Both buildings and their occupants can therefore be viewed as an opportunity but also a genuine challenge to energy saving. A classical approach in this context is to build knowledge models which are based on expert knowledge to model the energy behavior of buildings. However, designing knowledge models requires a great deal of expert time, significant knowledge of building specifications and a thorough physical background. Once designed, the calibration phase based on sensor data from the building is also a long and complex task. Finally, the slightest change in the environment, such as adding a ventilation system, would necessitate a parameters adjustment or even a re-design of the model. At the same time, while the occupants are not able to analyze and apprehend the energetic behavior of their housing, due to these complexities, they can decide on their comfort and expectations. Unfortunately, human behavior models are likewise tough to build, maintain and are still a challenging task. This research presents a new occupant-centered approach to enhance his indoor comfort using historical data on both the building environment and the behavior of its occupant, thanks to case-based reasoning (CBR). We evaluate the effectiveness of our technique on over one and a half years of real-world data collected from an office building. Results show that the proposed approach improves the comfort of the occupant.