Facility managers and buildings owners can use machine learning to control and, eventually, predict energy management use. That can save money on one of the largest financial factors of owning and managing a building.
Property and facilities managers can do that by asking, “How do I manage our HVAC systems based on the load that the building is experiencing?” notes Yardi vice president Akshai Rao, who will give a presentation about machine learning and energy management at the BOMA 2019 International Conference & Expo.
To look at energy management, the manager needs to consider all the variables that happen in a specific time period for a building and make appropriate decisions in terms of how the energy is consumed.
He gives the example of a building where hundreds of people gather in the main lobby every Tuesday at 9 a.m. With machine learning, the model starts to learn that this happens every week and prepares accordingly. Systems can decide before 9 a.m. to appropriately cool the space rather than take corrective measures after 9 when the temperature has elevated from the people gathering.
Machine learning “effectively looks at when should we be aware of load increases on a building and how should we act and be proactive ahead of these load increases,” Rao says.
Energy management solutions are the “perfect” use case for machine learning, Rao points out. Right now, machine learning in energy management is about looking at the inputs at very specific points in time and zones of a building.
If, for example, the ambient temperature is increasing, it assumes the load is increasing too and more people are coming into the area. To accommodate, systems react in specific and incremental ways to respond quickly and efficiently.
In the next few years, Rao suggests that machine learning can evolve to make more proactive and predictive decisions. The machine learning model could take information other information to determine how many people are in the building and where they might be. Examples of places it could pull information include:
- The example from the lobby every Tuesday morning
- Working with a scheduling system to see what meeting rooms will be in use
- Getting data from a parking gate about how many people are parked and might be in the building at the time
By not using machine learning for energy management solutions, building owners aren’t being as cost-effective as possible, Rao says. “By not managing more effectively, they are leaving a very large opportunity on the table. The owners [using machine learning] will be ahead and have excess returns,” he says.
Coupled with uncertainty of utility rates over the next 10 years in some areas, machine learning should be a consideration if not already in use.
Rao suggests that to start seeing savings, building owners follow these steps:
Every building is different and will need its own solution, so look at everything to see where the most opportunity exists.
“The only way you will know what buildings need it the most is through benchmarking,” Rao says. He suggests benchmarking internally and using an external benchmarking tool like ENERGY STAR to get an apples-to-apples comparison across the industry.
Once it’s determined what buildings or areas can benefit the most, bring in machine learning to reduce energy consumption where the return makes the most sense, Rao advises.
Incorporating machine learning in the energy management process can free property and facilities managers to focus on other parts of the building or manage more buildings. No matter what managers are doing with their time, Rao emphasizes that for energy management, human intelligence and context won’t go away.
“There is nothing that knows the building better than the chief engineer and property manager,” he says.