Artificial intelligence is something that is coming into almost every industry and now it is permeating HVACR. Technology is evolving and can aid in the effectiveness of work and preventative measures. Read about artificial intelligence and how it is impacting the HVACR industry.
When thinking about artificial intelligence (AI), usually what comes to mind are movies that depict computers or robots becoming self-aware and proceeding to torment humans. Think of HAL in “2001: A Space Odyssey” or Skynet in the “Terminator” movies. Thankfully, the reality is much different.
In fact, as far as the HVACR industry is concerned, AI is already providing opportunities to improve maintenance, comfort, and energy savings. For example, some AI-enabled systems use predictive modeling to foresee when a breakdown may occur, giving contractors time to fix the issue before it results in downtime. Other systems use AI to monitor and analyze conditions inside a space, as well as outdoors, and constantly make adjustments to the environment based on the data being collected. This leads to not only more comfortable occupants, but lower energy bills for building owners.
ONLY THE BEGINNING
AI was conceptualized back in the 1950s, but it is only through recent technological advancements that it has become more of a presence in our daily lives. For example, music and video streaming services created algorithms to predict consumer tastes and habits that have emerged into forms of AI that have really become a part of daily life without many people taking notice, said Hoppe.
“But within a commercial building, the power of M2M learning will really come into play as building owners become more aware about energy consumption and spending, comfort of the occupants, and the overall air quality inside the building,” he said. “Intelligent controls that use M2M learning could effectively provide recommendations to the building owner and technicians that result in less energy consumption, fewer HVAC repairs, and a potential lower cost of insurance due to catastrophic equipment failures.”
For technicians, the integration of AI could mean that they would no longer need to guess what operating parameters should be used after lengthy trial and error. Instead, intelligent controls could tell the technician what the problem with the equipment is and, more importantly, what to do about it, said Hoppe. To get to that point, though, AI will need to analyze massive amounts of data and a huge variety of scenarios.
That is why its implementation in the HVACR industry will initially be tied to more enterprise-level outcomes, broader system-level optimization, and limited focus on individual pieces of equipment or sensors, said Sinha.
“Keep in mind that AI gets better over time,” he said. “The commercial HVAC and controls industry is accustomed to certainty and predictability. AI introduces more agile learning and optimization methods of operating buildings and systems, and drives more continuous improvement. Eventually, AI will be used to find new boundaries for optimizations, as well as to predict new events and synthetic events that are not naturally extensible through data interpretation or alarm analysis of any one specific system, and then use external environmental factors to simulate the outcome of HVAC and control systems.”
As AI becomes utilized in a broader fashion, Wallace believes that more of the data processing will be performed at the equipment level (i.e., the controller on the equipment, sometimes called the edge device), which will lead to tighter integration of the controller installed in the equipment and a cloud-based service where the models would be created.
“To date, most of the work involving AI has been centered around gathering data from equipment and creating models to understand or predict if it is operating as intended for a given set of conditions,” he said. “For instance, if an abnormality is predicted or detected, a notification is provided, indicating that the piece of equipment may need attention of some sort.”
In order to create a predictive maintenance model that utilizes machine learning (a subset of AI), historical data will be required that details the operation of the equipment (e.g., status of the compressor, fans, temperature of the environment, etc.), as well as information about failures or operational problems that have occurred, said Wallace.
“While much of this data is present today, new sensors might be needed on the equipment to provide a more detailed prediction of its health and what actions should be taken to ensure it operates in an optimized fashion,” he said.
While AI-enabled products will no doubt continue expanding in the HVACR industry, a change in thinking may also be required. That’s because the primary challenge of designing HVACR products with AI is the same one faced by each company that integrates AI into its technologies, said Robb — recruiting talent with the skills and insight to create the AI-driven products of the future.
“This is, in part, due to challenges caused by generation gaps,” she said. “Older generations feel a level of confidence walking up to a thermostat and physically pushing a button. However, younger generations tend to expect that systems will evolve and adapt around them and that using voice commands and a generally hands-free approach to controlling the environment will become the norm.”