Abstract
Improving energy efficiency in the building sector is a subject of significant interest, considering the environmental impact of buildings. Energy efficiency involves many aspects, such as occupant comfort, system monitoring and maintenance, data treatment, instrumentation… Physical modeling and calibration, or artificial intelligence, are often employed to explore these different subjects and, thus, to limit energy consumption in buildings. Even though these techniques are well-suited, they have one thing in common, i.e., the need for user cases. This is why we propose to share a part of the large volume of data collected on our modular education building. The building is located on Nanterreʼs CESI Engineering school campus and welcomes approximately 80 students daily. A network of more than 150 sensors and actuators allows monitoring of the physical behavior of the entire building, preserving optimal comfort and energy consumption. The dataset includes the indoor physical parameters and the operating conditions of each system to describe the physical behavior of the building during a year.
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