In the recent past, you probably attended a virtual lunch-and-learn presentation, read an article, or had a discussion with a controls sales representative in which the topic was a chilled water plant optimization solution that uses some sort of cloud-based machine learning (ML) algorithms. What was your understanding of what optimization means? Similarly, what was your understanding of the ML algorithms that the aforementioned optimization solution used? I have no doubt the discussion was not short of claims (on behalf of the provider of the solution) of energy savings up to 30% and that the solution could probably work with any building automation system (BAS) in both new and existing buildings. Further, the discussion included, at most, very little details about how the energy savings are calculated or the type of machine learning algorithms used and their associated accuracy.
Today’s technological advancements have demonstrated the power of artificial intelligence (AI)-based control algorithms and their positive impact on the environment. However, it’s important to note that, just because a chilled water plant optimization solution is cloud-based and uses ML algorithms, it does not necessarily mean the solution will work as expected. To even begin to differentiate between various cloud-based chilled water plant optimization solutions, one needs to have a fundamental knowledge base about BAS (and their sequences of operation) and ML algorithms. First, one will need to understand that not all ML algorithms use the same type of data, i.e., discrete or continuous. For example and as it relates to building systems, the occupied or unoccupied status of the space throughout the day represents a set of discrete data, while the space temperature throughout the day represents a set of continuous data.