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The Importance of Data Modeling in Smart Buildings

Image of Patrick Gilhooly
Patrick Gilhooly

Data models visually represent the data gathered throughout a connected IT system, either in whole or in part to facilitate its storage with easy access from a database. In built environments, the importance of data modeling comes down to the ability of elements within this system to communicate. To better communicate, common definitions should show from where the data comes and what it represents. To ensure understanding and lessen confusion, rules are used to describe elements within the system.

While modeling building data can be done in many ways, it makes the most sense to align it around the requirements of the stakeholders who will utilize this data. In essence, a data model is comparable to an architect’s blueprint, a formal design that helps facilitate an understanding of how data works within the system. To be most useful, this architecture requires standardized protocols to describe the data. A good data model acts as a central directory from which definitions, default values, and rules are then defined.

Using good data modeling techniques in built environments ensures reliability and predictability by utilizing universal methods for defining and managing a building’s data resources. These data models should ideally be flexible enough to deal with future changes while also supporting efficient operations and functionality. Understanding the importance of data modeling in smart buildings allows for better analysis and understanding of this data, leading to more relevant insights.

Importance of Data Modeling in Built Environments

Smart technology within the built environment seeks to make buildings more efficient, comfortable, and convenient while streamlining operations. This requires harnessing the power of building data.

Data modeling looks to achieve:

  • Accurate representations of all data objects within a database to prevent faulty reporting and erroneous calculations
  • Conceptual, logical, and physical levels of design within a database.
  • Easier creation of a physical database
  • Economical and quicker upgrades and maintenance of IT infrastructure Identification of redundant and omitted data
  • The structure that assists in defining procedures for storing and organizing data, along with relationships within a database

A huge challenge in the building automation industry involves the vast differences in how data is named within these complex systems. Definitions are often different, depending on the manufacturer, vendor or other stakeholder describing the building data. To help with homogenizing these definitions, open-source standards have been developed to make working with building data more efficient. However, many of these standards fall short.

Types of Data Modeling for Smart Buildings

There are a number of ways in which to model data to organize it and help the data relate to physical objects and systems within the built environment.

The three main categories of data modeling are:

  • Conceptual data models focus on key associations and primary concepts, taking a macro view when it comes to definitions. As such, it describes what’s contained in the system and is used to define and organize concepts.
  • Logical data models are based on conceptual data models and are used to represent abstract structures, looking at relationships within data structures. These data models become the basis of physical data models once they’re validated and define how to develop data structures and map technical rules within a system.
  • Physical data models are specific to databases, representing relationships between data objects like columns, tables, and foreign and primary keys. It aids data analysis by describing how systems implement database management systems.

When it comes to the importance of data modeling within the built environment, a key approach involves the way in which the data is structured.

Importance of Semantic Data Modeling

Semantic data models are used to explain building data. They define and quantify relationships to enable more accurate building data analytics. Essentially, it’s a conceptual data model that enables vital information about relationships to be shared between data sets. These models incorporate fully open-sourced code to enable easier enhancement, examination, and modifications to databases.

By enforcing a common language between sensors and controllers, semantic data models define critical aspects of a building’s operations. These data models help translate data to a BAS or analytics software to enable more accurate analytics operations, along with easing the flow of data. As an open-source data model, it supports interoperability within the building.

Key aspects of semantic data models include their methods for creating a:

  • Vocabulary: Tags data with descriptive words like air, fan or unit.
  • Classification system: Categorizes words used to define such things as equipment and locations.
  • Relationships: Explains connections between equipment, such as how an air handle feeds a variable air volume system.

The importance of data modeling with such open-source methods cannot be over-emphasized, as it makes integrating new technologies into a building’s systems much easier and more economical.

Data Modeling: Integrating Smart Technologies & the OAP

A good data model assists building integrations by ensuring rules and structures regarding building data are implemented in a way that stakeholders are able to easily understand. They enable the use of analytical tools to assess a building’s operations, facilitating greater efficiency. Data models essentially support the integration of smart technologies into a building, promoting optimization of all a smart building’s connected systems.

However, the lack of standardization within data models has made integrating smart technology within the built environment more difficult. It is these data modeling standards, along with efforts to align these standards, that are currently driving the Ontology Alignment Project. By defining names and datapoints for connected equipment within a building, the OAP makes data easier to utilize. It also models specific relationships between different entities within a building’s systems to enable more efficient application of both root cause and system-based analytics.

Additionally, the OAP standardizes and normalizes building data from connected devices, applying it to methodical analytics. fault detection and diagnostics, as well as user interfaces. This allows the leveraging of building data to provide more effective management of a building’s operations. For master system integrators like Buildings IOT, the OAP provides a framework that will make the job of integrating smart technology into buildings easier.

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