ENTOUCH Smart Building Solutions

How Machine Learning Saves Energy & Maintenance Costs

Machine learning or ML is quickly rising on the radar for Facilities Managers looking for a better way to gain control over energy and maintenance costs. Facilities Managers need to understand the implications of machine learning, what it is, how it relates to energy and maintenance, and the additional benefits/functions possible through its use.

The Challenges of Managing Known Energy and Maintenance Costs

Managing energy and maintenance costs is a complex task that requires data, and while more companies are turning to smart sensors to generate data, they often come up short. In fact, consider these key challenges with managing energy, maintenance, and the data they generate.

  • Data in its raw form is meaningless and holds no immediate value.
  • Big data is just that “BIG”, which means the management of the vast amounts of data can be often seen as overwhelming to a facility professional whose department is already stretched thin.
  • Without effective management – mistakes may occur when adjusting system controls.
  • Real-time data may require immediate intervention to prevent the worsening of a facility or asset and thus negatively impacting the guest experience.
  • Legacy or home-grown technologies have some level of value, but machine learning associated with a smart building solution will amplify savings by squeezing every drop from predictive, preventive maintenance.
  • Data will identify unforeseen issues which could mean increased maintenance costs and exceed the established budget threshold for immediate repairs or deferment.
  • Without the effective use of actionable data – it’s difficult for a facility organization to know where it should be in terms of spending based on size, location, and occupancy.

Machine Learning Leverages Adaptable, Ever-Changing Algorithms to Achieve Cost Avoidance

Machine learning has become part of our lives. Smartphone assistants learn to recognize our voices and act on our commands. While this applies to a local or more “consumer-type” approach to using machine learning, it holds value in the application of energy and maintenance management as well.

Energy management is one of the most in-demand sectors of the economy. As little as four years ago, energy costs were significantly higher for the use of connected systems and technologies, reports Data Science Weekly. Today, advancements in computing capabilities have grown the power output to nearly 3.5-times more for the same amount of energy used.

Machine learning itself is a relatively simple principle. Instead of figuring energy and maintenance costs and schedules based on pre-defined, formula-based actions, machine learning allows for the ongoing evolution of the formula. Machine learning gives systems the ability to adapt to both internal and external factors, communicate with other systems, and achieve the desired result with fewer costs and delays compared to the most basic of analytics.

While machine learning can automate aspects of building management, it does not require companies to relinquish full control. Instead, users can still interact with the platform and ensure computer-generated insights align with realistic goals, which is especially important when considering energy-efficiency retrofits, making the repair or replacement decisions, and more.

Additional Benefits of Machine Learning Including Reduced Facilities Spend

Using machine learning in facilities management to reduce energy and maintenance costs offers additional benefits that arise from improved operations. These include:

  1. Reduced downtime and disruption to customer experiences.
  2. Better maintenance planning for tackling items that have fallen behind.
  3. Proactive project management to handle rebrands, renovations, or extensive needs.
  4. Machine learning ensures that predictions reflect the latest data and methodologies.
  5. Robust smart building solutions enable dynamic maintenance management, improving flexibility.
  6. Historical and real-time data improves reporting capabilities which empower automated facilities controls.
  7. Prioritization of maintenance and repair needs based on how deferred or immediate action will affect overall energy costs and system function.

Deploy a Smart, Intuitive, and Evolving Smart Building Solution Today

The Facility Management space is rapidly changing. Modern facilities management is being “smart” – comprised of connected assets and data-driven decision making. As more companies turn to the power of the Internet of Things (IoT) and big data analytics, using these smart technologies will begin to become commonplace. There is only so far that basic data from legacy technologies can push an organization, and machine learning from smart building solution is in the position to step in where those legacy technologies are limited and create immediate value. Learn more about how you can make your facilities “smart” and the value machine learning offers your facility organization by visiting ENTOUCH online today.