Related Articles

 

New Design and Synergistic Collaboration Revitalize Gallaudet University's Harkin Computer Lab - At Gallaudet University, the on-campus Harkin Computer Lab...

 

Advanced Technology Center Revolutionizes Learning Experience - Gulf Coast State College, in Panama City, Florida, has just...

 

Emergency Communication Platforms: The Latest in Integration Capabilities - When there's a threat to your university community, one of...

 

Automating Attendance, Visitor and Asset Tracking in University Environments: Taking Attendance Like Never Before - Universities are constantly advancing technology and...

 

Designing Future-Ready, Technology-Enhanced Rooms - Being smart and intentional when designing building...

 

Unlock the Secrets To Video Success On Your Campus - Everyone faces a fundamental need to communicate...

 

Archives > November 2015 > Harvesting Big Data for Operational Decisions

Harvesting Big Data for Operational Decisions

What exactly does big data look like? We've heard the buzz around Internet of Things (IoT) and the connected environment over the past few years. But in my experience, if you ask ten different people that question, you're likely to receive ten different answers.

By: Drew DePriest

Data exists all around you these days. It lives on your mobile device, in your Web browsing patterns, your social media accounts, and data lives in buildings. Monitoring, collecting, and aggregating data consistent in naming conventions, units, significant digits, non-null values, etc. poses a challenge in and of itself. Those with experience in building automation systems (BAS) know that BAS has effectively been doing IoT for decades.

The real value in harvesting big data for campus facility managers and energy managers lies in what comes next: How do you make better operational decisions as a result of what you learn from your data?

Speedometer vs Speed Limit

Picture yourself driving down the highway in your car. You know how fast you’re going thanks to feedback from your speedometer (measured data), and the speed limit sign you just drove past tells you how fast you should be going — this knowledge gives your measured data context. Without it, you’d have n
o idea how hard you should press your foot down on the accelerator.

Smart buildings must work in a similar fashion. There is a need to measure and understand every bit of information possible with regard to how a building consumes energy in real time — that’s the speedometer. But if there is not some kind of contextual sense for how much energy the building should be consuming (a speed limit sign), you won’t know when to take action to speed up or slow down the facility’s systems.

We’re seeing abig data trend in the connected world moving toward a smart building (integration of all mechanical and electrical systems) and, eventually, a smart city (integration of all systems of all buildings). The modern college campus serves as a great model for what the smart city could look like, as most campus buildings are already connected by a single BAS, working cooperatively to reduce aggregate energy consumption and cost.

In the case of a single BAS monitoring a large number of buildings, you’ll often find large data sets visualized in one of three ways to help drive three separate (yet related) types of decisions:

• Analytics (Operational decisions)

• Dashboards (Managerial decisions)

• Kiosk displays in public places

Analytics: Operational Decisions

Facility managers and building engineers benefit the most directly from analytics implementations in a smart building. The visualizations built for these roles tell the story of how a building’s mechanical and electrical systems are operating over time, including a real-time view into things like space temperaturbig data 3es, air flows through ductwork and status of pumps and fans.

Analytics should encourage technically-minded people to make tactical decisions to operate the components of their systems more efficiently. The smart building of the future will rely upon this concept greatly: If your analytics platform tells you a piece of equipment isn’t running as effectively as it should, you then have the knowledge to make an operational decision on how to improve its efficiency.

This could mean anything from editing an occupancy schedule to adding a setpoint reset to an existing control program. If it’s better operational decision-making and efficiencies you’re after, analytics can get you there. Think of your analytics engine as the speed limit and your actual performance data as the speedometer.

Building owners and managers globally have not fully embraced analytics just yet, though. In its 2015 World Building Automation and Control Systems study released in August, research firm BSRIA estimates only 10 percent of all BAS installations worldwide include an analytics component. It’s accurate to say we’re only seeing the earliest adoption of deeper machine learning analytics at this time.

Dashboards: Managerial Decisions

If you’re a property manager or an energy manager for a large facility or campus, you probably won’t want to see every single bit of data monitored throughout every building. Most likely, your primary concern would be to optimize the control of your building in order to maximize occupant comfort and minimize energy consumption. This can be achieved by viewing and responding to data concerning the overall energy consumption of your facilities.

Energy-focused dashboards take metered data such as electric, gas and water consumption, and display it in such a way that a non-technical person can understand a building’s energy profile. Dashboards generally also provide a means of normalizing energy data across metrics like total square footage, number of occupants, and degree days, which account for the impact of more extreme hot or cold days. People with access to these dashboard visualizations can then make managerial decisions to control those energy costs and know where to invest in future efficiency upgrades.

Kiosks: Behavioral Decisions

The most difficult variable to manage when it comes to energy consumption, not surprisingly, is the human element. Loads from computers, monitors and anything else plugged into an outlet, can account for as much as 30 percent of a building’s energy use, according to the EPA’s ENERGY STAR program. The challenge for facility managers and building owners then becomes, how do you encourage your occupants to reduce their energy consumption through changes in their habits?

A kiosk is a great tool for occupants to use. Think of a kiosk as a dashboard that’s public-facing, interactive, and gives your audience context as to how your facility consumes energy. The thought behind the kiosk is that by showing students, faculty, staff and visitors how your facilities use energy, you can encourage them to contribute to reducing overall consumption.

big data kiosk

As a caveat, it shoudl be noted that the presence of a kiosk in and of itself will likely not result in any drop in energy usage. Sustainability campaigns, to have any sort of effect, need to be driven and maintained at a high level. While such campaigns might certainly include a kiosk as part of their delivery, the kiosk should also be accompanied by other messaging and means for promoting energy conscious behavior.

Gamification efforts that involve a competitive aspect, such as having residence halls compete to reduce energy consumption, can help to accomplish that goal at a wide level. The more you can enga
ge your campus community as partners in your effort to reduce energy consumption, the better chance you stand of success.

Perpetual Challenge

Energy management, particularly on a university campus, presents a perpetual challenge for those tasked with reducing energy consumption. Data stands to play a strong role in unearthing deeper opportunities for savings beyond the “low-hanging fruit” of measures like lighting retrofits. The key is to identify problematic areas and turn to data for insight, not start with data and look for a problem.

 

 

About The Author
Drew DePriest

is a Regional Sales Manager who brings over eleven years of experience to the building automation industry. He has worked at Automated Logic since 2004, working in previous challenging roles such as energy solutions engineer, project manager, project engineer, and field engineer.

 

 

PUPN Magazine is a trademark of Flaherty Media, LLC, copyright 2017. PUPN Magazine and all contents are properties of Flaherty Media, LLC.