Dataset of an operating education modular building for simulation and artificial intelligence

Area of Science:

• Building Science
• Energy Efficiency
• Sustainable Architecture

Background:

• Buildings significantly impact the environment, making energy efficiency crucial.
• Current methods for improving building energy efficiency often require real-world data for validation.
• Occupant comfort and system monitoring are key aspects of building energy management.

Purpose of the Study:

• To provide a comprehensive dataset from a modular educational building.
• To support research in physical modeling, artificial intelligence, and data-driven approaches for energy efficiency.
• To facilitate the development of strategies for optimizing building energy consumption and occupant comfort.

Main Methods:

• Deployment of over 150 sensors and actuators for comprehensive building monitoring.
• Collection of a year's worth of data on indoor physical parameters.
• Recording of operating conditions for all building systems.

Main Results:

• A detailed dataset capturing the building's physical behavior over one year.
• Data includes indoor environmental parameters and system operational status.
• The dataset enables analysis of energy consumption patterns and comfort levels.

Conclusions:

• The shared dataset serves as a valuable resource for the building energy research community.
• Facilitates the creation and validation of advanced energy efficiency models.
• Contributes to advancing sustainable practices in the building sector.