Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.

The research areas of occupant sensing and occupant behavior modeling are lacking comprehensive public datasets for providing baseline results and fostering data-driven approaches. This data descriptor covers a dataset collected via sensors on room-level occupant counts together with related data on indoor environmental quality. The dataset comprises 44 full days, collated in the period March 2018 to April 2019, and was collected in a public building in Northern Europe. Sensor readings cover three rooms, including one lecture room and two study zones. The data release contains two versions of the dataset, one which has the raw readings and one which has been upsampled to a one-minute resolution. The dataset can be used for developing and evaluating data-driven applications, occupant sensing, and building analytics. This dataset can be an impetus for the researchers and designers to conduct experiments and pilot studies, hence used for benchmarking.

Faults in buildings systems affect energy efficiency and occupancy comfort. Simulating building behavior and comparing it with measured data allows to detect discrepancies due to faults. We propose a methodology to recursively compare actual data with dynamic energy simulations at different layers of aggregation to reduce the scope in searching for faults through the development the Online Energy Simulator, a tool to set up automated simulations using standard interfaces usable with different building systems and simulation engines. We test our simulator on a real building at the University of Southern Denmark, showing how continuous monitoring allows to quickly detect and identify buildings faults.

Often manually performed commissioning processes on building's sensors fail to systematically validate that all building's sensors operate correctly. This is so because manual processes are tedious and only inspect a limited number of sensors. As a result, sensors are often uncalibrated, biased or somehow faulty, impacting building's behaviour, comfort level and energy usage. We present a practical approach to automatically validate data from all building's sensors. We designed and implemented four different tests to detect out-of-range values, spikes, latency issues and non-monotonous values. Our tests are based on expert knowledge and do not need historical data. We ran the validation tests on a newly constructed building at the campus of the University of Southern Denmark. As a result we identified two types of faulty behaviours in the building's sensors: CO ₂ sensors reporting biased values and temperature sensors' readings exhibiting high latency. We show how automatic data validation for building sensors enhances the processes of detecting issues which could severely impact building's operations, and were otherwise going unnoticed. Thus, we emphasize the importance of performing data validation as a necessity for a correct building operation.