Jicheng Shi
This dataset was gathered for the RISK project (SNSF grant number 200021 17562) and NCCR Automation project (SNSF grant number 51NF40_180545). If you use it for your own research, remember to cite
@article{shi2022data,
title={Data-driven input reconstruction and experimental validation},
author={Shi, Jicheng and Lian, Yingzhao and Jones, Colin N},
journal={IEEE Control Systems Letters},
volume={6},
pages={3259--3264},
year={2022},
publisher={IEEE}
}
Other related papers
- Y. Lian, J. Shi, M. P. Koch, and C. N. Jones, "Adaptive robust data-driven building control via bi-level reformulation: An experimental result," IEEE Transactions on Control Systems Technology (2023). DOI: 10.1109/TCST.2023.3259641 Template Code
- Y. Lian, J. Shi, and C. N. Jones, "Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters," IEEE Control Systems Letters 7 (2023) 1885 - 1890. DOI: 10.1109/LCSYS.2023.3282987
- J. Shi, Y. Lian, C. Salzmann and C. N. Jones, "Adaptive Data-Driven Predictive Control as a Module in Building Control Hierarchy: A Case Study of Demand Response in Switzerland", arXiv preprint arXiv:2307.08866 (2023). arXiv:2307.08866
HVAC system data from an entire building, called Polydome. The time-series dataset (12k+ points) is collected in two seasonal periods (summer and winter) with a 15-minute sampling frequency. In particular, part of the dataset contains the occupancy and CO2 values (1k+ points).
Besides, it contains the template code for the paper "Data-driven input reconstruction and experimental validation".
The database is built based on risk-br's structure by Emilio Maddalena.
Polydome is a freestanding
In the Polydome, a roof-top heat pump, AERMEC RTY-04, serves as an HVAC system. It can execute ventilation (~2.4kW), heating (~6kW), and cooling (~4.6kW). The heat pump collects both the external air and the return air from the room side. When the heat pump is open, it keeps pumping the air to the room for ventilation. The flow rate of the ventilation air is roughly constant. At the same time, it can heat or cool the air before pumping. The heating and cooling procedure is switched on/off according to the difference between the return air temperature (room side) and the corresponding setpoint. By setting the heating (cooling) setpoint 1 degree higher (lower) than the return air temperature, one compressor in the heat pump starts to heat (cool) the air. If this difference is more than 2 degrees, two compressors begin to work together. But this condition should be avoided because the internal controller for the two compressors is not well designed.
The people in the building are counted by Jicheng Shi, not by other advanced sensors. The reason is that the authors hope to quickly construct the occupancy measurement by CO2 levels with short-term data and without some complex sensors, e.g., cameras. But the manual counting leads to some errors. For example, on the night of Friday, 10-12-2021, the building should be occupied (maybe by a student party) based on the CO2 levels, but Jicheng had already left. In addition, four air quality sensors are installed evenly at four corners. The sensor distribution can be found in Folder/zwave_overview.jpg.
From a control perspective: The system inputs are power, (return_temp-supply_temp)*supply_flow. The outputs are sensor_temp_1, sensor_temp_2, sensor_temp_3, sensor_temp_4. The disturbances are weather_temp, weather_rad.
The electrical power of the heat pump is measured every one minute. The value of power represents the average electrical power within each sampling period. When mode=1 (0), the heat pump executes the heating (cooling) mode, and the power is negative (positive). One may also compute the heating or cooling energy by (return_temp-supply_temp)*supply_flow. Four temperature sensors are installed in different places: two on the 1.5-meter-high walls, two on the top of the small rooms (>2.5m) in the Polydome. In system identification, one may use the average to attenuate the measurement noises. The external weather data are collected from a weather API, tomorrow.io. It offers an accurate weather estimation at 2 meters above the ground for any specific longitude and latitude. The current dataset was collected during the summer vacation, so occupancy should be zero.
When was it collected? Two long-term periods: from July 15, 2021, to September 8, 2021; from October 31, 2021, to January 14, 2022. One short-term period including occupancy: from December 6, 2021, to December 19, 2021.
Sampling period: 15 minutes.
📁 dataset/data: contains the data files.
raw_2021-07-15_2021-09-08.mat: a structure exp in Matlab that contains the collected data.
raw_2021-10-31_2022-01-14.mat: a structure exp in Matlab that contains the collected data.
raw_06-12-2021_19-12-2021.mat: a structure exp in Matlab that contains the collected data (occupancy included).
📁 dataset/docs: contains some pictures to illustrate the system architecture.
📁 code/IRO: contains an example code for the paper "Data-driven input reconstruction and experimental validation".
time_str [GMT-0]: date and time of the current measurements. Note it records the time of UTC, not the Swiss time.
time [GMT-0]: datenum(time_str) in Matlab. Note it records the time of UTC, not the Swiss time.
sensor_temp_1 [deg Celsius]: temperature inside room: 1.5-meter-high wall.
sensor_temp_2 [deg Celsius]: temperature inside room: 1.5-meter-high wall.
sensor_temp_3 [deg Celsius]: temperature inside room: top of a small room in the polydome (>2.5m).
sensor_temp_4 [deg Celsius]: temperature inside room: top of a small room in the polydome (>2.5m).
power [kW]: the average electrical power of the heat pump within each sampling period. Positive: cooling; negative: heating.
supply_temp [/10*deg Celsius (this value should be divided by 10 when transformed to 'deg Celsius') ]: the supply air temperature of the heat pump on the room side.
return_temp [/10*deg Celsius (this value should be divided by 10 when transformed to 'deg Celsius') ]: the return air temperature of the heat pump on the room side.
supply_flow [*10m^3/h (this value should be multiplied by 10 when transformed to 'm^3/h' )]: the supply air flowrate of the heat pump on the room side.
weather_temp [deg Celsiu]: outdoor temperature measured by the weather API: tomorrow.io.
weather_rad [W/m^2]: solar radiation (global horizontal irradiance) measured by the weather API: tomorrow.io.
mode : 1: heat pump in heating mode; 0: heat pump in cooling mode.
setpoint_cool [deg Celsius]: temperature setpoint of the heating mode for the heat pump.
setpoint_heat [deg Celsius]: temperature setpoint of the cooling mode for the heat pump.
co2_1 [deg Celsius]: CO2 level measured by 1 air quality sensor.
co2_2 [deg Celsius]: CO2 level measured by 1 air quality sensor.
co2_3 [deg Celsius]: CO2 level measured by 1 air quality sensor.
co2_4 [deg Celsius]: CO2 level measured by 1 air quality sensor.
people [ person]: number of occupants manually counted by Jicheng.