LinkedIn Uses Data Analytics to Lower Carbon Emissions by 84t of CO2 and OPEX Costs by an Estimated $100,000

In June 2016, an expert building operations team deployed data analytics to uncover rate, use, and demand savings to be implemented over a 12-month period.

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Industry Commercial Real Estate
Property Details
  • Over one million SF of commercial office space in northern California
  • Top-notch working environments in suburban office and downtown tower
  • Fuel cells, permanent and mobile electric car charging stations
Challenges Managing the energy spend across a diverse collection of building types—with technology occupants who might turn up at all hours of the day or night—and with building vintages spanning from decades to just a few days is quite complex.
Solution Snapmeter for automated load curve monitoring and peak demand forecasting, Billcast for utility bill monitoring including variance and rate analysis, and RCx for balance-point analysis, building-to-building comparisons, and heatmaps for instant daily load curve trend visualizations.
Why Gridium
  • Automatic data collection from utility means no hardware in buildings, nor any BMS integration delays
  • Peak demand forecasts and anomalous use alerts emailed to engineering team every Monday morning: web-based dashboard means monitoring from anywhere
  • Automated utility bill analytics paired with load curve analytics make rate optimization accurate and easily quantifiable

The Challenge

How can a proactive operations and engineering team, responsible for nearly a million square feet of top-notch commercial office, manage electricity costs while simultaneously exceeding occupant comfort and safety expectations across a portfolio of diverse building environments? And on top of this, the team was responsible for LinkedIn’s move into a brand new downtown office tower.

The Solution

Gridium’s software for cloud-based smart meter data analytics does not require any hardware or software to be installed onsite. This made it easy to launch a free trial and immediately identify thousands of dollars worth of OPEX savings.

Billcast analyzed years of monthly bill data to uncover a handful of rate optimization opportunities, totaling nearly $50,000 (est.). Snapmeter’s automated baseload analytics helped the operations team achieve around $50,000 (est.) in savings from advanced baseload and demand management. Snapmeter’s weekly emailed load curve analytics also helped reduce sharp demand spikes, and smooth out certain Monday-morning hard starts. Optimal off hours operations dropped one building’s nightly baseload by 70 kW, and load curve analytics helped engineering staff identify and diagnose faulty VFDs.

The office environments here at LinkedIn are truly world-class. It’s with great pride that our engineering and operations teams work towards world-class building operations as well. The GHG reductions and tens of thousands of dollars of OPEX savings achieved with the help of Gridium analytics reflect this approach.

Claudia Rodas, Director Bay Area Workplace Operations, LinkedIn