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Big Data Analytics & Green Buildings

June 17, 2014 | 0 comments

You’ve probably heard the term “big data,” or been involved in conversations about big data analytics. These phrases are becoming popular ways to describe the growth and accessibility of structured (easily searchable, highly organized) and unstructured (text-heavy, not easily searchable) data.

McKinsey & Company defines big data as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.” In other words: Big data analytics goes beyond basic spreadsheets that track utility bills.

The reason big data analytics is getting so much attention in our industry? Because of its ability to help accurately analyze building practices. Precise analysis can aid in removing guesswork from decision-making. Relevant data gathered from sensors, building controls, and submeters can be combined, searched, and used to:

  • Determine root causes of equipment failures, issues, and defects
  • Identify performance variances between floors
  • Make informed decisions about how to better manage buildings
  • Benchmark and compare building performance

Although reducing energy usage comes down to tenant behavior, efficient equipment, and green building practices and design … it ultimately starts with data. It’s hard to reduce energy or water use if you don’t understand what’s being wasted in the first place (or how that usage stacks up comparatively). And when these metrics and data are combined with results from tenant/occupant surveys, security system footage, and IT network data (just to name a few other sources), the effects can be powerful.

Big data is also being used to conduct remote energy audits. Without entering the property, thousands of data points can be collected through a building’s address and annual utility data. Combining an address with big data allows factors affecting a building’s climate to be identified, such as humidity, wind, solar radiance, and precipitation. Through GIS mapping, a building’s size and physical characteristics can also be pinpointed. According to FirstFuel, an organization that offers a remote building analysis platform, information like scheduling, sequencing of equipment, and temperature setpoints can be deduced through the data. All of this data is then analyzed and used to establish plans for improving energy efficiency.

Do you have a plan for handling big data to manage energy efficiency? Are you already using big data to make decisions?


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