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Data Makes Building Operations More Efficient

July 11, 2018

Using data to drive building maintenance is more efficient than simply relying on assumptions or rules of thumb. 

The recent history of building operations management has been defined by the steady improvement of the tools operators and engineers can use to inform decision-making. Building information modeling and other analytical models, whether data or physics-based, have had significant positive impacts on the industry.

Nevertheless, the American Council for an Energy-Efficient Economy study found that most commercial air conditioners in the U.S. are oversized by 25-50%. In fact, subsequent studies have only counted units as oversized if it is more than 25% oversized because that is widely considered a “safe and acceptable practice.”

The progress hasn’t been sufficient. A shift is needed from the “art” of intuition, assumptions and rules of thumb to the “science” of empirical evidence driving statistically significant conclusions.

Fortunately, the Internet of Things (IoT) has unlocked the potential to collect real-time empirical data about the individual components that make up a physical infrastructure of buildings. Armed with enormous data sets, analytics software can focus on benchmarking, comparisons and identifying patterns and anomalies from observed performance.

Most of the low-hanging fruit has been picked; measuring and analyzing actual performance is necessary to continue to eliminate waste in capital investments, maintenance and repairs, and energy consumption.

Even robust building management systems (BMS) that use continuous data inputs to directly control equipment usage are prone to waste through intuition and assumptions. Improperly configured BMS are believed by the Office of Energy Efficiency & Renewable Energy to account for 20% of building energy usage (about 8% of total energy usage in the U.S.).

Let’s follow the lives of two commercial air conditioning units.

Oversized Air Conditioner Fails

The first one was installed and maintained according to industry best practices. The second one was installed and maintained using empirical data from circuit-level electrical demand sensors.

When designing the first system, the engineers used rule of thumb calculations to ensure that it guarantees enough cooling capacity to satisfy tenant requirements.

As is common, the system is 25% oversized.

The preventative maintenance schedule, based on the manufacturer’s recommendations, dictates that the unit be serviced once per year in early spring before the cooling season.

After installation, tenants grumble to themselves that the indoor temperature varies wildly when it’s hot out. They find that when the air conditioner kicks in, it gets very cold quickly and then gets warm before jumping down again. They also notice that it seems to stay humid even when the air is cold.

Eventually, the operators get a complaint from a tenant during the summer that its space isn’t cooling at all.

After an investigation to determine the root cause of the issues, operators uncover that the unit has been short cycling. They check the levels of refrigerant, test the thermostat to ensure it’s reading correctly and is appropriately placed, and make sure the low-pressure control switch and compressor are working properly.

Everything appears to check out, so the operators determine the unit is likely oversized and is cooling the space too quickly, cycling on and off quickly to maintain the desired temperature.

Popular article: 5 Steps to Optimize Your Facility with Data Analytics

While the solution is to replace the unit, there’s no room in the capital expense budget, so the operations team decides there’s nothing they can do except increase the amount of preventative maintenance checks during the cooling season.

During one of the maintenance checks, the team forgets to set the unit back to its original schedule afterward. At another point, the operators change the set point to meet the desires of a particular tenant, but never change it back.

For the rest of its life, the unit wastes energy by running when the building is unoccupied and outside air temperature is relatively low.

After 15 years, the unit fails entirely, and a replacement is required.

Benchmarking for the Correct Unit

Now let’s look at the life of the second air conditioning unit.

When designing the system, the engineers used equipment-level benchmarking to determine the right unit by considering factors such as make and model, climate, sizing and occupancy schedule. The benchmark was built by tracking and recording millions of machine hours of air conditioning units across the portfolio and aggregating data from other equipment in the same region and building vertical.

As the manufacturer recommends, the operators perform a preventative maintenance check in the early spring before the cooling season.

In addition, the operators receive period notifications when the electrical demand of the unit indicates that conditions necessitate servicing the unit.

When the unit inevitably needs repair, a fault detection alert directs operators to the unit, avoiding an investigation and enabling them to fix the system before tenants notice.

If at any point, the system configurations are changed to a suboptimal schedule or balance point, the operators receive a notification that uses inferred occupancy and weather data to prescribe the optimal settings.

The unit lasts its full 20-year lifetime before a replacement is required.

What’s the Difference?

The second scenario results in reduced costs and improved tenant comfort. It also requires continuously tracking electrical consumption at the circuit level. This is an investment but one that pays for itself many times over when all the avoided costs are added up:

  • Capital investment costs: Lowered by right-sizing equipment rather than paying for unnecessary capacity.

  • Maintenance costs: Lowered by reducing the number of unplanned maintenance work orders, eliminating the time spent investigating tenant complaints and avoiding arbitrary additional maintenance checks.

  • Energy costs: Lowered by maintaining the optimal schedule and set points instead of letting performance drift over time.

  • Net present value of money: Because future money is less valuable than money today, value is added by delaying equipment replacement for five years. In those five years, money can be used to invest in revenue generation, energy-saving retrofit projects or used to collect interest.

  • Tenant experience: Improved by ensuring normal air quality and by proactively addressing issues, potentially affecting leasing decisions.

The cost of an IoT-based equipment energy tracking solution is generally an order of magnitude lower than a traditional building management system. Although these systems can’t control equipment like a BMS, the data-driven decision making enabled by these solutions present an attractive investment, whether there is a BMS in place or not.

Experienced operators and engineers will always be necessary to ensure that systems run properly. However, these professionals should favor hard data over assumptions whenever possible. Because of the IoT, empirical data at the equipment level is affordable and relatively easy to acquire.

The more we can direct building operations from an art to a science, the healthier our indoor environment will be, the easier operators’ jobs will be and the more profitable real estate will become.

Connell McGill is the CEO and Co-Founder of Enertiv, a data analytics company focused on streamlining building operations to reduce costs and improve tenant comfort. Recently, McGill was named one of the “Top 10 Energy Entrepreneurs in NYC” by Breaking Energy for his contributions at Enertiv. Prior to founding Enertiv, McGill was a Senior Project Manager at the New England Consulting Group, consulting for a number of C-suite executives at Fortune 100 companies. 

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