Intelligent Building Agents Project Data

The Intelligent Building Agents (IBA) project is part of the Embedded Intelligence in Buildings Program in the Engineering Laboratory at the National Institute of Standards and Technology (NIST). A key part of the IBA Project is the IBA Laboratory (IBAL), a unique facility consisting of a mixed system of off the shelf equipment, including chillers and air handling units, controlled by a data acquisition system and capable of supporting building system optimization research under realistic and reproducible operating conditions.The database contains the values of approximately 300 sensors/actuators in the IBAL, including both sensor measurements and control actions, as well as approximately 850 process data, which are typically related to control settings and decisions. Each of the sensors/actuators has associated metadata. The metadata, sensors/actuators, and process data are defined on the "metadata", "sensors", and "parameters" tabs in the definitions file. Data are collected every 10 s.The database contains two dashboards: 1) Experiments - select data from individual experiments and 2) Measurements - select individual sensor/actuator and parameter data. The Experiments Dashboard contains three sections. The "Experiment Data Plot" shows plots of the sensor/actuator data selected in the second section, "Experiment/Metadata". There are plots of both scaled and raw data (see the meta data file for the conversion from raw to scaled data). Underneath the plots is a "Download CSV" button; select that button and a csv file of the data in the plot is automatically generated. In "Experiment/Metadata", first select an "Experiment" from the options in the table on the left. A specific experiment or type of experiment can be found by entering terms in the search box. For example, searching for the word "Charge" will bring up experiments in which the ice thermal storage tank is charged. The table of experiments also includes the duration of the experiment in minutes.Once an experiment is selected, specific sensor/actuator data points can be selected from the "Measurements" table on the right. These data can be filtered by subsystem (e.g., primary loop, secondary loop, Chiller1) and/or measurement type (e.g., pressure, flow, temperature). These data will then be shown in the plots at the top. The final section, "Process", contains the process data, which are shown by the subsystem. These data are not shown in the plots but can be downloaded by selecting the "Download CSV" button in the "Process" section. The Measurements Dashboard contains three sections. The "Date Range" section is used to select the time range of the data. The "All Measurements" section is used to select specific sensor/actuator data. As in the Experiments Dashboard, these data can be filtered by subsystem and/or measurement type. The scaled and raw values of the selected data are then plotted in the "Historical Data Plot" section. The "Download CSV" button underneath the plots will automatically download the selected data.

                                            [
  "006:55"
]
                                        

                                            {
  "fn": "Amanda Pertzborn",
  "hasEmail": "mailto:amanda.pertzborn@nist.gov"
}
                                        

                                            [
  {
    "title": "IBAL Database",
    "format": "Database",
    "accessURL": "https://ibal.nist.gov",
    "description": "The database contains the values of approximately 300 sensors/actuators in the IBAL, including both sensor measurements and control actions, as well as approximately 850 process data, which are typically related to control settings and decisions. Each of the sensors/actuators has associated metadata. The metadata, sensors/actuators, and process data are defined on the "metadata", "sensors", and "parameters" tabs in the definitions file. Data are collected every 10 s."
  }
]
                                        

                                            [
  "Building control systems; Heating",
  "ventilation and air conditioning equipment; Machine Learning; Applied AI;"
]
                                        

                                            [
  "en"
]
                                        

                                            [
  "006:045"
]
                                        

                                            {
  "name": "National Institute of Standards and Technology",
  "@type": "org:Organization"
}
                                        

                                            [
  "https://doi.org/10.1016/j.est.2019.03.011",
  "https://doi.org/10.1109/TNNLS.2021.3085358",
  "https://doi.org/10.3384/ecp21181177",
  "https://doi.org/10.6028/NIST.TN.1933",
  "https://doi.org/10.6028/NIST.TN.1970",
  "https://doi.org/10.6028/NIST.TN.2025",
  "https://doi.org/10.6028/NIST.TN.2037",
  "https://doi.org/10.6028/NIST.TN.2178"
]
                                        

                                            [
  "Buildings and Construction:Air conditioning and heating equipment",
  "Buildings and Construction:Building control systems"
]