How IoT & AI Enable More Efficient Energy Use in Commercial Office Buildings

Written By: Eric Nelson

This is the 1^st post in a series on “Optimizing Commercial Building UX, Asset Health, and Energy Conservation with IoT and AI,” leading up to
my talk on April 25^th at IoTFuse2019.

 

How Much Energy is Wasted in my Portfolio per Year?

What is the Primary Source of Waste?

How Much Money Could I Save by Optimizing Energy
Spend without Sacrificing Tenant Comfort?

These are a couple questions which can be asked when managing or assessing operational costs of a commercial property. Billions of $USD are spent each year operating commercial office buildings. Most are still operated based on simple automation rules, with more complex behavior relying on manual procedures. There are, however, pioneering companies who are building networks of sensors and pattern-learning software to monitor and automate sophisticated building behavior which saves costs while improving the tenant experience.

What is the Typical Energy Cost Profile for Commercial Office Buildings in the U.S.?

The U.S. Energy Information Administration (EIA) does a periodic survey of energy consumption in commercial buildings (called CBECS) in all 50 states. In 2015, they released the results of the 2012 survey. There’s another survey currently underway for CY 2018, but, clearly, it’ll be a while until those results are released.
**Note: All estimated qualities are extrapolated from a representative sample size of ~6,700 surveyed buildings. Read more about CBECS Methodology & Research

In 2012, there were over 1 million U.S. commercial office buildings, enclosing nearly 16 billion square feet, in operation for an average of 55 hours per week. Annually, total energy delivered to those building costs around $31 billion. That’s electricity ($26B), natural gas ($2.3B), district heat ($1.8B), and fuel oil (0.5B). “District heat” is hot water or steam piped out to buildings from a central heat generation plant. It tends to be more prominent in the Mid- and South-Atlantic regions. And ancient Rome.

Electricity expenditures obviously dwarf those of the other energy sources. So where does all that energy go? Almost 25% goes to Ventilation, ~20% to Computing, ~17% to Lighting, ~13% to Cooling, and the remaining 25% goes to other office equipment, refrigeration, space heating, and, of course, the eponymous Other stuff that isn’t tracked. Lighting used to be a bigger consumer, but more CFLs replaced a lot of incandescent bulbs over the last 10 years and many conventional F40 fluorescent tubes have been replaced by models up to 20% more efficient.

We don’t have more detailed data on the top energy consumers (Ventilation & Computing, totaling 45%), but we do have Lighting and Cooling data, totaling 30% — or about $8 Billion annual electricity spend. Here’s some interesting usage information:

1. Nearly 40% of all commercial office buildings are 100% lit when the building is on.
2. Over 60% of all buildings cool 100% of the space when the building is on.

That includes vacant offices and conference rooms, as well as mostly-vacant large open areas. We don’t have the data, but we can presume the numbers for cooling are probably similar for ventilation. I.e. Perfectly good air is being vented out of vacant rooms.

The majority of remaining buildings are lit and cooled 50-99%. It’s safe to presume there are some inefficiencies in these buildings as well.

The Problem with Current Control Technology is that it’s Difficult to Make Buildings More Efficient without Making them Less Comfortable.

The typical Building Management System (BMS) operates on relatively simple rules (turn on at this time and turn off at this time) to control relatively large sections of the building. Oftentimes, the only way to ensure tenant comfort is to turn everything on well in advance of working hours.

So that’s the control problem. But there’s also a logic problem. It’s difficult to specify rules for how human tenants will use a commercial office building. A room left vacant one day may be occupied the next day. More bodies may be in the building on Wednesday than Friday. Conducting a survey of when people work, and which rooms they use most frequently would give temporary insight, but we could never specify such detailed rules into a Building Automation System. The rules would change too frequently, and inevitably, tenant comfort would suffer as rule changes lagged behind behavior.

We’d prefer to take appropriate action based on real-time, measured information. E.g. How many bodies are in the building right now, radiating heat and exhaling CO₂ to vent? What is current room temperature and CO₂ level? What is the ambient temperature outside? Is anyone using Conference Room A?

Actually, because it takes time to cool & vent spaces, we’d really like to take action based on an accurate prediction of the near-future. E.g. Given it’s a Thursday, how many bodies will be in the 3^rd floor atrium in 30 minutes? Who will be using Conference Room A in 20 minutes and what temperature setpoint do they like?

So, we measure the building data at high detail, and the we learn rules for managing it at a similar level of detail. Logic and control.

How Does an IoT and AI Solution Reduce the Building Energy Cost Profile?

Building data comes in through a fairly dense swarm of sensors, which can be installed, for example, in the ceiling, on the same power line and data bus as the lighting system. The sensors measure occupancy, temperature, CO₂ levels, and lighting output. Sensor clusters are then mapped to XY coordinates in a floor plan. Conference rooms and other enclosed spaces will have their own sensor clusters, and so on. This network of sensors forms a “nervous system”, informing a building manager of current status at high detail in real-time.

Other sensors measure the rate of volumetric air being moved in and out of each space, the amount of energy is expended to cool that air to a temperature setpoint, and which chillers are consuming what proportions of that energy. Temperature and CO₂ readings near each vent will be strongly correlated to its airflow readings. There is a math model for how changes to airflow will change nearby temperature and CO₂ levels.

With sensors in place, we can run experiments to learn a unique model for each vent. Naturally, spaces near doors and other high human throughput zones will have different relationships than relatively unperturbed spaces. And the relationships will change over time as human behaviors change and asset performance degrades. Thus, models and rules will need to be continuously re-learned. Each relationship, however, gives us high-resolution levers to pull to get the comfort outcomes our tenants want. All while turning on as little of the building as possible, minimizing costs.

The aim is for no building to ever be 100% cooled, 100% vented, or 100% lit, unless it is 100% occupied. Our next step to that end is to define a strategy for initiating deployment of IoT and AI in commercial office buildings, both in new construction and retrofitting existing stock.

But Does this Technology Really Work in Practice?

There is a growing number of success stories in implementing IoT and AI in commercial office buildings. Here are a few.

Rudin Management Company collaborated with Columbia University back in 2010 to build Di-BOSS (Digital Building Operating System) in order to read data from, and remotely control, all the different equipment in their commercial buildings. Eventually Intel came in and migrated the database from SQL to NoSQL and from prem to cloud, in order to scale the solution beyond a single building. A year later, it was running 17 buildings. And according to Intel, energy use dropped by 9% and costs went down nearly $1M in the first year in just one building. Not only that, but hot/cold dropped by 70%. The OS, now called Nantum, “forecasts and proactively predicts optimal settings for HVAC and other systems”, and Rudin sees annual savings of 30% energy consumption and over $5 million cost saving across the 17 buildings.

In 2014, Deloitte Netherlands, in collaboration with OVG Real Estate, built what would come to be known as “the greenest, most intelligent building in the world.” Known as The Edge in Amsterdam, the new construction was rated the world’s most sustainable building by BREEAM, scoring 98.36% across 10 sustainability metrics (the highest ever awarded at the time). A primary strength of the building design is a swarm of 28,000 sensors monitoring lighting, air quality, temperature, and occupancy. This real-time data is fed into a custom monitoring and usage pattern-learning application developed by Deloitte. According to Deloitte, “Long term emerging patterns showing light use of certain locales on certain days can lead to rooms or even entire floors being closed off to save energy.” The resulting energy profile can be sustained almost entirely by 5900 m² of rooftop solar panels.

Energy saving, of course, is only the beginning of the benefits from real-time monitoring of commercial office buildings. For example, when Teradyne implemented Watson IoT and Tririga from IBM, they included sensors from monitoring contaminate levels in the heating and cooling water loop. IBM also cites instances of forecasting end-of-life for large assets (like air handlers), enabling better financial management and less down time for business maintenance. The Edge in Amsterdam credits hot-desking (where workspaces are booking on-demand vs permanently allocated – because they’re often vacant) with increasing effective capability by more than 140%.

It Sounds Good. How Would I Get Started?

The strategy for implementing IoT and AI in commercial office buildings has roughly 7 parts:

1. Define the objectives for the technology, based on an anticipated ROI, and the intended implementation schedule.
2. Design the network of sensors and IoT gateways to embed into the building.
3. Collect real-time sensor data into both structured and unstructured data stores.
4. Model relationships between inputs and outputs using regression and other machine learning techniques and then deploying those models for on-demand interface.
5. Expose both raw sensor data and inferences to user-facing applications via API.
6. Design easy-to-use, high-productivity user interfaces for building managers and occupants.
7. Continuous improvement of inference models using reinforcement learning.

AND that’s where we’re heading next!

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About the Author

Eric Nelson is a Sr. Software Engineer with ArganoMS3, Inc., living in Minneapolis, MN with his wife Alisa, 6yo daughter Freyda, and 4yo son Arthur. Eric received his BS in Electrical Engineering from University of Minnesota and worked in photovoltaics & thin films, both at MN tech start-ups. In 2013, Eric helped found an inner-city charter middle school and taught courses in technology and entrepreneurship. In 2015, he founded his own cloud software consulting firm Augment LLC. And began training student cohorts in software design & development skills. He is now focused on building large-scale, secure, futureproof production software systems for smart brands as a member of the ArganoMS3 family. Meanwhile, he continues his effort to develop a scalable programmer training network for high school and college students. He also serves on the board of Minnesota Innovates, a nonprofit dedicated to cultivating MN technology startups building products in emergent technologies (e.g. AI, AR/VR, Gene-editing, Drones, etc.)