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How Artificial Intelligence is Changing Automated Demand Management for the Better

Facilities managers face new issues every day when running their buildings. They work around the clock to keep their building running, but there are many factors that can get in their way:
  • They have a large number of people and systems to manage.
  • When a system breaks down, that becomes their first concern.
  • They constantly have to balance cost efficiency with the needs of their client base.
A part of the challenge is monitoring their buildings, and this is where the difficulties begin. Facilities managers lack the adequate technology and resources to effectively monitor their building’s energy usage. They’re not able to analyze their energy peaks, and as a result, their energy bills skyrocket.
Without the right information, they’re left to simply guess their systems’ problems. This is  difficult, as it conflicts with what facilities managers need to do - keep their building operating. They need to a method to monitor energy usage without wasting time, money, and resources to make that happen.  

How AI Enables Better Automated Demand Management


Verdigris recently published
a white paper detailing how our artificial intelligence technology can help solve these problems. In particular, using AI for Automated Demand Management (ADM) can cut costs drastically, without up-front capital investment or any manual intervention by building staff.
Verdigris’ innovative solution combines three critical components to deliver sustained, consistent reductions in building peak demand:
  1. Highly granular, circuit-level electrical data, captured in real-time, 24/7, by our sophisticated sensors
  2. Advanced hour-ahead forecasting, enabled by our rich data stream that incorporates hyper-localized weather feeds, occupancy info, and utility pricing signals
  3. Direct connection into your existing Building Management System using BACnet protocol
Buildings with time-of-use pricing tariffs are frequently spending up to 50 percent of their energy bills on demand charges – the peak amount of power (in kilowatts, or kW) that you consume at one time over any 15 minute period – and there are few reliable methods to enable proactive management of these peaks.
Under classic Automated Demand Response (ADR) programs, companies will work with their utility companies to set up a program to curb their demand charges during peak periods, agreeing to pay incentives of as much as $200-400 per kW.
The downside comes from the fact that utility companies retain the control over your systems with this program. Utility companies control when these events happen, and the covered systems are all-on or all-off.
Using artificial intelligence, Verdigris developed a technology called Automated Demand Management (ADM) to give control back to facilities managers. This method uses real-time forecasting, optimization, and data unification to manage your energy peaks - a step above responding. It dynamically cycles equipment to keep the building under a pre-determined demand threshold, lessening occupant impact and reducing demand – and costs – whether the utility has asked for it or not.
After developing this technology, we deployed it to a large, luxury hotel in San Francisco in July 2016. In the first month, our technology detected and curtailed 43 unique demand events, with an average of 31 kW in savings. Our largest reduction was 42kW – over $1,500 in savings at the above Peak rates.
Overall, the hotel saved 22 percent in monthly energy costs. The hotel continues to use ADM technology and has continued to save consistent cost savings as a result. The more time our technology spends in the building, the more savings our customers will see. The use of this technology has given the hotel more time to focus on guest comfort and to increase their operational capabilities. They’ve put the control of their building back in their hands.
To learn more about our partnership with the hotel and more about the problems our customers have been facing, read our white paper on the subject here.
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