So what is a “smart” building? Often the term is confused with the addition of a building automation system in place of an antiquated pneumatics system, or when an engineer develops a new controls strategy that lessens the energy consumption of a central plant. But what defines a “smart” building? The definition of smart is having or showing intelligence, which is achievable through the merger of data analytics and building automation systems. The evolution of analytic software has been incorporated into building automation systems, providing operators and end users more insight into how equipment functions than ever before. Analytics are now run on everything from variable speed supply fans to local heating valves on units; all in an attempt to register impending failure and deliver actionable data to the end user. How many tenant complaints are based on the failure of a valve or damper actuators? What if a thermostat slipped out of calibration and went unnoticed? What if the software could predict the malfunction and generate a work order on its own? These are all examples of the application of analytics within a building. Analytics are powerful tools that when incorporated into building automation can not only predict impending failures, but deliver those results in unique ways to end users.

Software analytics was derived from the time consuming task undertaken by workers in the mid 90’s to predict the future of equipment performance based on a varied criterion of conditions. If the conditions were met failure could be prevented, saving the end user from unnecessary expenses. So what if this process was automated? How powerful would the combination of analytics and building automation be in your building? Take for example an air handler where the outside air temperature is 75 degrees, return air temperature is 70 degrees, and supply air temperature is 65 degrees. During these conditions the supply fan, per designed conditions and estimated baseline energy consumption, normally runs at 60% and the cooling valve is opened to 35%. Would a normal BAS system alarmed if the fan’s run rate increased to 63% and the valve opened to 40%? Without a comparison to past criterion could your BAS decipher which equipment was working abnormally and alert you? One of the core features of data analytics is data mining to identify system malfunctions that normally wouldn’t be understood by a typical building automation system; a critical feature that is a basis for large energy savings.

Building automation analytics have now also bridged the gap between financial officers and facility engineers. Many property owners are faced with increasing energy bills in conjunction with higher emission regulations; looking to their maintenance staff to help reduce costs. Typical buildings have also already taken advantage of low hanging fruit or “easy” energy reductions available like installing Variable Frequency Drives (VFD’s) or undertaking lighting upgrades. Are there any other strategies that could help squeeze every kilowatt of energy savings out of your equipment? Predictive maintenance, identified in the above example, is one of the key measures to help compare real-time equipment operation to energy consumption costs. What if you could see the financial loss associated with a failing component over the course of a day? Through analytics packages, such as the one installed at the PUC in San Francisco, the end user will not only be notified of an equipment malfunction but also see the financial impact of not repairing the issue. Therefore, financial officers can take a proactive approach to energy consumption by reviewing the analytic BAS from a fiscal perspective and reporting to the facility engineers; an approach unattainable by traditional BAS implementations.

[an error occurred while processing this directive] Data analytics remain a unique solution based on the distinctive characteristics of each building. No two buildings are designed or maintained in the same manner due to size, location, structure, etc. Unlike configurable software, analytics must be designed on a job-to-job basis to ensure accuracy of results and highest return of investment. Take for example two buildings, one in Phoenix versus another in San Francisco. A building operator in San Francisco is concerned more with heating spaces and high humidity while Phoenix lacks humidity and deals and with higher, dry heat loads. The prerequisite variables necessary to predict equipment malfunction and optimize performance are going to be vastly different. Due to these principles, the integration of data analytics comes at a greater investment than the typical automation integration, but with higher returns. A number of smart building consultants estimate energy savings through properly installed analytics at 15%-20%, over 6%-8% through standard automation and simple monitoring alone. Factor in operational savings and tenant retention due to high satisfaction rates and the returns grow even higher.

Analytics have become one of the prominent buzz words in the engineering community behind LEED and green energy due to the associated savings. Often property owners can utilize their existing BAS system with a new front end such as the Tridium A/X which is capable of hosting analytic platforms. This type of installation will allow the analytics platform to harvest data from disparate systems and manipulate equipment directly from within the program framework; a unique solution offering greater expandability into sequence upgrades based on how equipment has functioned in the past. Integrated analytics packages are helping to evolve the world of smart buildings through continuous management of energy consumption.

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