{"id":997,"date":"2021-05-04T18:25:25","date_gmt":"2021-05-04T18:25:25","guid":{"rendered":"http:\/\/3.92.44.188\/?p=997"},"modified":"2021-05-04T18:32:00","modified_gmt":"2021-05-04T18:32:00","slug":"clockworks-information-model","status":"publish","type":"post","link":"https:\/\/clockworksanalytics.com\/clockworks-information-model\/","title":{"rendered":"Clockworks Information Model"},"content":{"rendered":"\n

Consistent Building Analytics at Scale<\/h2>\n\n\n\n

Last week\u2019s post, SH*TTY DATA IN \u2260 SH*TTY DATA OUT<\/a> introduced Clockworks\u2019 dynamic information model and the approach that makes it the most robust and conclusive in the industry. That\u2019s a bold claim, and this blog backs up our boldness. We\u2019ll dive deep into each component of the information model and show why it\u2019s required to enable successful fault detection and diagnostics (FDD).\u00a0<\/p>\n\n\n\n

Before we begin, let\u2019s start with an analogy for how the building industry is currently classifying data from a Building Management System (BMS). Consider the English language. Project Haystack<\/a> provides the equivalent of a dictionary. It gives you words and, to an extent, the definitions of those words. It\u2019s mostly limited to nouns: coil, fan, point. Brick Schema<\/a> gives you words too\u2014it provides the verbs in the dictionary. For example, this thing feeds air to this other thing. Or this thing feeds water to this other thing.\u00a0<\/p>\n\n\n\n

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Unfortunately, we need more than just nouns and verbs to speak English. We need additional rules to make our words form sentences and convey meaning. Taking it back to modeling building system data, FDD requires an information model that is built to convey this extra meaning in a consistent and machine-readable format. Clockworks\u2019 information model does just that, enabling our analytics to scale up without relying on human translators.<\/p>\n\n\n\n

To begin, let\u2019s introduce the five parts of the Clockworks Information Model one by one and show how they\u2019re applied to a standard air handling unit (AHU), AHU1 with 14 simple analog input (AI) points from a BMS controller.<\/p>\n<\/div><\/div>\n\n\n\n

1. Equipment Templates<\/strong><\/h4>\n\n\n\n

Clockworks defines equipment, such as an air handling unit, by a name, such as \u2018AHU1\u2019, but also by a predefined equipment template, which is described by its class and type. <\/p>\n\n\n\n

For example, AHU1 would belong to an equipment class, such as \u2018Air Handling Unit\u2019, and an equipment type, such as \u2018Air Handling Unit with Economizer\u2019.<\/p>\n\n\n\n

\"AHU-equipment-template\"
Air Handler Unit- Equipment Template<\/sup><\/figcaption><\/figure>\n\n\n\n

Classifying the equipment in this way aligns it with a predefined template in Clockworks. The template implies certain expected behaviors such as that it delivers air, that it has an economizer, that it may perform heating and\/or cooling, that it has fans, and that it should be associated with specific components and data points. <\/p>\n\n\n\n

With the template applied and data points discovered, we can now analyze those data, right? Not so fast. The problem with buildings is that they’re all snowflakes. There is no golden template for any of our equipment types, so we must get more descriptive.<\/p>\n\n\n\n

2. Equipment Variables<\/h4>\n\n\n\n

In the real world, we might need to manipulate the equipment template to say, \u201cwell, maybe this preheat coil is actually over here in front of the mixing box.\u201d<\/p>\n\n\n\n

\"clockworks-equipment-variable\"
Air Handler – Equipment Variable<\/sup><\/figcaption><\/figure>\n\n\n\n

Something as simple as shifting that coil location could completely change the way this piece of equipment needs to be analyzed. The way the air is moving throughout this unit and the position of every single component really matters to the FDD analysis. <\/p>\n\n\n\n

That\u2019s where metadata we call Equipment Variables come into play. <\/strong>These variables can be configured to define geometry, as shown above, or represent concepts like the expected behavior and engineering characteristics of the equipment, as shown below.<\/p>\n\n\n\n

\"equipment-variable-behaviors-connections\"
Equipment Variable With Behaviors and Characteristics<\/sup><\/figcaption><\/figure>\n\n\n\n

As shown in orange, equipment variables could define the rated air flow of the air handler in cubic feet per minute (CFM) or the expected discharge air temperature control strategy such as \u2018Reset by Return Air Temperature (RAT)\u2019. These are important properties that can be represented by predefined equipment variables such as \u2018HasSupplyTempRAResetSchedule\u2019.<\/em><\/p>\n\n\n\n

3. Point Types<\/strong><\/h4>\n\n\n\n

Similar to equipment templates, Clockworks defines points, such as a \u2018SA_Temp\u2019, by a predefined point class and point type. For example, the \u2018SA_Temp\u2019 may belong to AHU1 and be of type SupplyAirTemp in the class Temperature. Point types are integrated into the equipment templates so a user can direct the discovered data points from the BMS to reserved spots within the template, as shown below in blue:<\/p>\n\n\n\n

\"Clockworks-point-types\"
Clockworks – Point Types<\/sup><\/figcaption><\/figure>\n\n\n\n

In other words, the specific combinations of point types uniquely identify the point and define its purpose within the equipment operation. This is analogous to Project Haystack\u2019s concept of point prototypes<\/a>. <\/p>\n\n\n\n

As you can see, even with points mapped to a specific equipment template and variables applied, there are still many gaps in the data model that need to be filled in to enable robust and scalable FDD. Let\u2019s proceed.<\/p>\n\n\n\n

4. Relationships<\/strong><\/h4>\n\n\n\n

Relationships between objects exist to define how points, equipment, and systems are related and should be expected to behave. <\/p>\n\n\n\n

Within a piece of equipment like an AHU relationships can exist between components of the AHU itself to determine how individual setpoints and sensors control the valves and dampers providing heating, cooling, and ventilation. Relationships between sensors, setpoints, and the logic embedded within the controller itself must be included in the information model to accurately model the equipment operation.<\/p>\n\n\n\n

In Clockworks, relationships can be automatically inferred based on the equipment template and point types selected. In the example below, Clockworks will infer the AHU is utilizing a differential drybulb economizer sequence based on the selected equipment type (AHU with Economizer) and the combination of Point Types selected (no enthalpy points available, but return and outside air temperature are available). Clockworks will also infer the unit is designed to dehumidify by cooling\/reheating based on the user-provided ReturnAirRHMax<\/em> Equipment Variable and a lack of alternative dehumidification points on the unit.<\/p>\n\n\n\n

\"building-equipment-relationships\"
Point, Equipment, and System Relationships<\/sup><\/figcaption><\/figure>\n\n\n\n

Physical relationships also exist between the AHU and other equipment in the building. These relationships define how hot and chilled water is fed to the AHU from the chiller and boiler plants, and how the air from the AHU is fed to the various VAV boxes throughout the areas the AHU is serving. Clockworks models these physical connections as well, allowing users to create parent\/child relationships between equipment and uses those relationships to improve the analysis capabilities on each piece of connected equipment automatically.<\/p>\n\n\n\n

In the AHU example below, the Chiller\/AHU, Boiler\/AHU, and AHU\/VAV parent\/child relationships are established:<\/p>\n\n\n\n

\"parent-child-equipment-relationships\"
Parent\/Child Equipment Relationships<\/sup><\/figcaption><\/figure>\n\n\n\n

In some cases, defining relationships among equipment can signify a system. For example, a chilled water plant can be created as its own \u2018system\u2019 level equipment which contains child equipment such as chillers, pumps, heat exchangers, cooling towers, and hydronic loops.<\/p>\n\n\n\n

5. Automatically Calculated Points<\/strong><\/h4>\n\n\n\n

Finally, Clockworks calculates the remaining gaps in the equipment template automatically. It uses logic to combine what we know about the piece of equipment to create new data points that look and feel just like actual control points and help us do a better analysis, as shown in green in the figure below:<\/p>\n\n\n\n

\"Clockworks-calculated-points\"
Calculated Point Types<\/sup><\/figcaption><\/figure>\n\n\n\n

This includes data like the real-time power on a fan when it’s not being measured. Or the energy load on a coil. We calculate these points because the typical building management system (BMS) isn’t going to produce these more advanced outputs on its own, and yet, they\u2019re vital for a higher level engineering analysis. <\/p>\n\n\n\n

This approach, built up over a decade of deploying FDD at scale, goes far beyond simply tagging points from the BMS. The flexibility and comprehensiveness of the information model within Clockworks enables our FDD analytics to run across 400,000,000 sq.ft. of buildings everyday\u2014without having to write custom fault rules or equations to deliver consistent diagnostic reports.<\/strong><\/p>\n\n\n\n

As you can see, the Clockworks information model\u2014which combines equipment templates, point types, inferred relationships, and calculated data\u2014 provides the most robust and holistic approach to data modeling in the industry. As we mentioned at the start, it is still an important step for the broader industry to develop a standard data \u201clanguage.\u201d We have participated in the ASHRAE Standard 223P<\/a> committee, and are excited to work with Project Haystack and the Brick Consortium to continue to share our dynamic approach to information modeling to help drive the industry toward a universal standard. For Clockworks Analytics, accelerating data interoperability standards underscores our commitment to help the industry-at-large turn operational data into real-time insight for healthier buildings in a scalable way.<\/p>\n","protected":false},"excerpt":{"rendered":"

Consistent Building Analytics at Scale Last week\u2019s post, SH*TTY DATA IN \u2260 SH*TTY DATA OUT introduced Clockworks\u2019 dynamic information model and the approach that makes it the most robust and…<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_tec_requires_first_save":true,"_EventAllDay":false,"_EventTimezone":"","_EventStartDate":"","_EventEndDate":"","_EventStartDateUTC":"","_EventEndDateUTC":"","_EventShowMap":false,"_EventShowMapLink":false,"_EventURL":"","_EventCost":"","_EventCostDescription":"","_EventCurrencySymbol":"","_EventCurrencyCode":"","_EventCurrencyPosition":"","_EventDateTimeSeparator":"","_EventTimeRangeSeparator":"","_EventOrganizerID":[],"_EventVenueID":[],"_OrganizerEmail":"","_OrganizerPhone":"","_OrganizerWebsite":"","_VenueAddress":"","_VenueCity":"","_VenueCountry":"","_VenueProvince":"","_VenueState":"","_VenueZip":"","_VenuePhone":"","_VenueURL":"","_VenueStateProvince":"","_VenueLat":"","_VenueLng":"","_VenueShowMap":false,"_VenueShowMapLink":false,"_tribe_blocks_recurrence_rules":"","_tribe_blocks_recurrence_description":"","_tribe_blocks_recurrence_exclusions":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-997","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"acf":[],"yoast_head":"\nClockworks Information Model - Clockworks Analytics<\/title>\n<meta name=\"description\" content=\"Clockworks\u2019 dynamic information model is the most robust in the industry. 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