This post is a continuation of our series of blog posts highlighting presentations from the 2nd Edition of Seville Machine Learning School (MLSEV). You may also check out the previous posts about the 6 Challenges of Machine Learning or Predicting Oil Temperature Anomalies in a Tunnel Boring Machine.
Given that Machine Learning has been established as a foundational piece of technology similar to RDBMS, it can touch every business or engineering process to power data-driven automated decision making in near real-time. Indeed, in our everyday life as consumers, we interact with Machine Learning systems unknowingly in many familiar contexts.
In this vein, as part of the real-world use case presentations during MLSEV, Delio Tolivia from Talento Corporativo, a Spanish consulting firm providing a wide array of business solutions, presented their optimization project to minimize elevator wait times. The primary goal of the project was to improve on the standard passenger experience by making elevators smarter. Their client for this project was Thyssen Krupp AG, which is a German multinational conglomerate with a focus on industrial engineering and steel production. With millions of elevators around the world, it’s not too hard to see how the scale of this problem and its potential impact can be very large in its future iterations.
To begin the Talento team gathered data from 5 different elevator systems and split those into 3 groups based on the different types of buildings they operated in, the usage patterns demonstrated such as traffic levels during weekdays vs. weekends, and frequency of trips between floor pairs.
Based on extensive data exploration efforts, the Talento team decided to go with a simpler rule-based approach to program the behavior of those elevators with very consistent usage patterns over the time period analyzed. This is very much in line with our philosophy at BigML, which has driven us to preach the value in trying simpler approaches first as those can save a lot of time and energy while forming meaningful baseline comparisons for future Machine Learning endeavors on the same business problem.
They achieved a 12% reduction in energy consumption with one of the rule-based subgroups of elevators with more straightforward operating characteristics. Then, they proceeded to build Machine Learning models on the BigML platform to better understand and optimize the behavior of the elevator with more complex operational characteristics. Their early results were very promising as they observed an 8% reduction in passenger wait times even though the initial set of experiments were deployed for hotel elevators that did not perfectly match the type of elevator producing the data their OptiML and Classification Ensemble Models were originally trained on.
Without further ado, let’s take a more in-depth look into this industrial use case. You can click on the Youtube video below and/or access the slides freely on our SlideShare channel:
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