{
  "_schema": "https://data.nist.gov/od/dm/nerdm-schema/v0.7#",
  "@context": [
    "https://data.nist.gov/od/dm/nerdm-pub-context.jsonld",
    {
      "@base": "ark:/88434/mds2-2751"
    }
  ],
  "@type": [
    "nrdp:PublicDataResource",
    "dcat:Dataset"
  ],
  "_extensionSchemas": [
    "https://data.nist.gov/od/dm/nerdm-schema/pub/v0.7#/definitions/PublicDataResource"
  ],
  "@id": "ark:/88434/mds2-2751",
  "ediid": "ark:/88434/mds2-2751",
  "version": "1.0.1",
  "doi": "doi:10.18434/mds2-2751",
  "title": "Intelligent Building Agents Project Data",
  "contactPoint": {
    "fn": "Amanda Pertzborn",
    "hasEmail": "mailto:amanda.pertzborn@nist.gov"
  },
  "modified": "2022-06-14",
  "status": "available",
  "landingPage": "https://data.nist.gov/od/id/mds2-2751",
  "description": [
    "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.\nThe 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.\nThe 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.\nOnce 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."
  ],
  "keyword": [
    "Building control systems; Heating",
    "ventilation and air conditioning equipment; Machine Learning; Applied AI;"
  ],
  "topic": [
    {
      "@type": "Concept",
      "scheme": "https://data.nist.gov/od/dm/nist-themes/v1.1",
      "tag": "Buildings and Construction: Building control systems"
    },
    {
      "@type": "Concept",
      "tag": "Buildings and Construction:Air conditioning and heating equipment"
    }
  ],
  "accessLevel": "public",
  "license": "https://www.nist.gov/open/license",
  "publisher": {
    "name": "National Institute of Standards and Technology",
    "@type": "org:Organization"
  },
  "language": [
    "en"
  ],
  "bureauCode": [
    "006:55"
  ],
  "programCode": [
    "006:052"
  ],
  "theme": [
    "Buildings and Construction: Building control systems",
    "Buildings and Construction:Air conditioning and heating equipment"
  ],
  "references": [
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.6028/NIST.TN.2178",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.6028/NIST.TN.2178",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "Baseline Control Systems in the Intelligent Building Agents Laboratory",
      "issued": "2022-09-22",
      "citation": "Pertzborn, A. J., & Veronica, D. A. (2022). Baseline Control Systems in the Intelligent Building Agents Laboratory. https://doi.org/10.6028/nist.tn.2178\n"
    },
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.3384/ecp21181177",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.3384/ecp21181177",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "An Ice Storage Tank Modelica Model: Implementation and Validation",
      "issued": "2021-09-27",
      "citation": "Guowen Li, Yangyang Fu, Amanda Pertzborn, Jin Wen, & Zheng O\u00e2\u0080\u0099neill. (2021). An Ice Storage Tank Modelica Model: Implementation and Validation. Proceedings of 14th Modelica Conference 2021, Link\u00c3\u00b6ping, Sweden, September 20-24, 2021. https://doi.org/10.3384/ecp21181177\n"
    },
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.1109/TNNLS.2021.3085358",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.1109/TNNLS.2021.3085358",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "Reinforcement Learning Based Optimal Tracking Control Under Unmeasurable Disturbances With Application to HVAC Systems",
      "issued": "2021",
      "citation": "Rizvi, S. A. A., Pertzborn, A. J., & Lin, Z. (2021). Reinforcement Learning Based Optimal Tracking Control Under Unmeasurable Disturbances With Application to HVAC Systems. IEEE Transactions on Neural Networks and Learning Systems, 1\u00e2\u0080\u009311. https://doi.org/10.1109/tnnls.2021.3085358\n"
    },
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.6028/NIST.TN.2037",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.6028/NIST.TN.2037",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "Measurement uncertainty of the air system in the intelligent building agents laboratory",
      "issued": "2019-03",
      "citation": "Pertzborn, A. J. (2019). Measurement uncertainty of the air system in the intelligent building agents laboratory. https://doi.org/10.6028/nist.tn.2037\n"
    },
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.6028/NIST.TN.2025",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.6028/NIST.TN.2025",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "Intelligent building agents laboratory: air system design",
      "issued": "2018-09",
      "citation": "Pertzborn, A. J., & Veronica, D. A. (2018). Intelligent building agents laboratory: air system design. https://doi.org/10.6028/nist.tn.2025\n"
    },
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.1016/j.est.2019.03.011",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.1016/j.est.2019.03.011",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "Using distributed agents to optimize thermal energy storage",
      "issued": "2019-06",
      "citation": "Pertzborn, A. (2019). Using distributed agents to optimize thermal energy storage. Journal of Energy Storage, 23, 89\u00e2\u0080\u009397. https://doi.org/10.1016/j.est.2019.03.011\n"
    },
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.6028/NIST.TN.1970",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.6028/NIST.TN.1970",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "Measurement uncertainty in the hydronic system in the IBAL",
      "issued": "2017-09",
      "citation": "Pertzborn, A. J. (2017). Measurement uncertainty in the hydronic system in the IBAL. https://doi.org/10.6028/nist.tn.1970\n"
    },
    {
      "@type": [
        "npg:Document"
      ],
      "@id": "#ref:10.6028/NIST.TN.1933",
      "refType": "IsSupplementTo",
      "location": "https://doi.org/10.6028/NIST.TN.1933",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"
      ],
      "title": "Intelligent Building Agents Laboratory: Hydronic System Design",
      "issued": "2016-09",
      "citation": "Pertzborn, A. J. (2016). Intelligent Building Agents Laboratory: Hydronic System Design. https://doi.org/10.6028/nist.tn.1933\n"
    }
  ],
  "components": [
    {
      "accessURL": "https://ibal.el.nist.gov",
      "format": {
        "description": "Database"
      },
      "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.",
      "title": "IBAL Database",
      "@type": [
        "nrdp:AccessPage",
        "dcat:Distribution"
      ],
      "@id": "#",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/pub/v0.7#/definitions/AccessPage"
      ]
    },
    {
      "accessURL": "https://ibal.nist.gov",
      "format": {
        "description": "Database"
      },
      "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.",
      "title": "IBAL Database",
      "@type": [
        "nrdp:AccessPage",
        "dcat:Distribution"
      ],
      "@id": "#",
      "_extensionSchemas": [
        "https://data.nist.gov/od/dm/nerdm-schema/pub/v0.7#/definitions/AccessPage"
      ]
    }
  ],
  "annotated": "2023-09-21T18:20:10.107070",
  "revised": "2022-12-02T01:41:08.815431",
  "issued": null,
  "firstIssued": "2022-12-02T01:41:08.815431",
  "releaseHistory": {
    "@id": "ark:/88434/mds2-2751.rel",
    "@type": [
      "nrdr:ReleaseHistory"
    ],
    "hasRelease": [
      {
        "version": "1.0.0",
        "issued": "2022-06-14",
        "@id": "ark:/88434/mds2-2751/pdr:v/1.0.0",
        "location": "https://data.nist.gov/od/id/ark:/88434/mds2-2751/pdr:v/1.0.0",
        "description": "initial release"
      },
      {
        "version": "1.0.1",
        "issued": "2022-06-14",
        "@id": "ark:/88434/mds2-2751/pdr:v/1.0.1",
        "location": "https://data.nist.gov/od/id/ark:/88434/mds2-2751/pdr:v/1.0.1",
        "description": "metadata update"
      }
    ]
  }
}