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Data and Building Automation Systems

Accessing data from existing building automation systems is critical to any smart building project. We have gone from the challenge of equipment and system connectivity (now a given) to one where it is about data collection and processing and the integration of data from diverse systems and devices. Add to this, the techniques for managing, presenting, analyzing and deriving value from this data.

Building automation architecture is continuing to flatten as more devices directly connect to networks and edge devices are becoming more intelligent. These devices are performing data aggregation, data integration, and routing directly to other operational devices. Today’s devices are equipped with faster processors, more memory, a selection of connectivity and capacity options to support a variety of applications with the ability to go beyond simple connectivity to include configuration, management, data storage and device-level application enablement.

With our ability to access data from building and operational systems we have moved from connected devices to connected intelligence and are redistributing and processing data independently at the edge device level. We have moved from vertical, single purpose devices to ones that are multi-purpose and function in a collaborative environment within the building.

With the increasing number and variety of equipment, sensors, devices and building systems available to connect to and the amount of data that is available from them, we have seen many data projects stall or never reach their potential because companies struggle with the complexity of the data. While data is technically available, the challenge lies in working with it across multiple applications, managing it and getting useful information out of it especially as data sets come with various formats, different naming conventions, and syntaxes. It's one thing to have access to data; it's another to make it actionable.

Today data is not connectivity issue or a cost one. We need ways to gather and analyze data effectively as well as efficiently. If there are any obstacles there is the lack of planning, lack of naming convention use and data validation.

When it comes to planning, most building operations do not have a unified data management plan. What passes for a “data management plan” consists of a database associated with the Building Management System. With that approach the information is limited to just those systems monitored or managed by the building management system.

Data planning takes a look at all the operational data and information required in order to manage a building’s performance. Identify the data and information that different people and groups involved with the building’s performance need to perform their work. Much of the data will be monitoring points on building systems but some data may be needed is in business systems or other systems outside of facility management or even outside the organization. Identify where the data exists; how it must be accessed, collected, how it will be exchanged and estimate the volume. Decide on a format---use naming conventions and modeling.

When it comes to the data, it is one thing to have access to it; it's another to make it actionable. With more data available than ever before the industry is presented with a new challenge. Device data is stored and communicated in many different formats. It has inconsistent, non-standard naming conventions, and provides very limited descriptors to enable us to understand its meaning. Simply put, the operational data from smart devices and equipment systems lacks information to describe its own meaning. Without meaning, a time consuming manual effort is required before that data can be used effectively to generate value. The result is that the data from today’s devices, while technically “available”, is hard to use, thus limiting the ability for building operators to fully benefit from the value contained in the data.

Standardize what you call things. A multi-building campus with buildings built at different times with different contractors is likely to have multiple names and tags for similar pieces of equipment. You don’t want to end up with ten different names for air handlers or pumps. Multiple naming conventions in an existing building or portfolio of existing buildings is the largest and most time consuming issue involved with implementing an integrated building management system.

One naming convention to look at is Project Haystack (www.project-haystack.org).Project Haystack is open-source community utilizing tags and semantic data models to define and describe the meaning of data from smart devices of all types and enables software applications to automatically consume, analyze and present data from devices and equipment systems.

There’s no point in collecting inaccurate data. To get the most accurate information you’ll need to ‘tune-up” the building systems and check the calibration of sensors and meters. The building systems themselves should be regularly re-commissioned.

Data is a corporate asset and empowering companies to seek and make good fact-based decisions that drive better outcomes. Connecting to it; collecting it, storing it, insuring its integrity; analyzing it, and using it to make business decisions and develop strategy. Determining who controls and who owns the data and what is done with it will lead us down some interesting paths. Furthermore, as data velocity is on the rise, companies must be able to rapidly analyze it and get actionable advice instantaneously. It is not about more data, but rather asking the right questions to get the right data, understand it and help solve specific problems and address specific issues.