This article is sponsored by Redaptive.
Businesses depend on data analytics in most areas of their operations. Analytics are vital to all sorts of tasks, including personalizing the customer experience, entering new markets and driving innovation and product development.
But one area where businesses are not fully leveraging data is in sustainability efforts. This is not ideal but also not surprising. Most companies don’t devote a lot of mindshare to energy consumption, much less their carbon output, because they don’t consider it core to their business.
That’s a mistake. Companies should be using data analytics to manage their energy consumption because it not only reduces their carbon footprint, but it can also save a lot of money.
Here’s an example. When you set the thermostat for an office, you might think you’re doing a good job because none of your employees are complaining about it being too hot or too cold. What you don’t realize is that you could be running your heating and cooling simultaneously, consuming twice the necessary energy and putting more wear and tear on your equipment. In fact, about 30 percent of energy is wasted in the average commercial buildings. That is a huge potential savings.
Data provides insight to control costs
There are three areas where data can offer valuable insights. The first is energy usage. Data can show when the lights are not shutting off on schedule. It can reveal if heat is running in the middle of the night when no one is in the building.
The second is maintenance. Waiting to fix your HVAC system can be expensive and risky, especially if it ends up failing. Data will warn when performance is starting to degrade, and specific maintenance tasks need to be done. Then you can proactively make the necessary repairs and avoid paying for work that isn’t required. A large auto shop, for instance, uses air compressors to service vehicles. If an air compressor goes down, the shop can’t repair cars and loses revenue. But if the shop uses data to monitor its air compressors and provide feedback on their performance, it can avoid downtime by doing preventive or even predictive maintenance.
The third area is operations. Let’s say I’m running a chain of dialysis centers. I would want to know if one of my dialysis machines is operating irregularly. Maybe one is running the same number of hours as the machine next to it but using three times more electricity. Analyzing usage data such as that will provide valuable insights and help me make informed decisions about how to manage all my dialysis equipment to avoid downtime, cut energy consumption and reduce patient risk.
Smart meters provide the data you need
So how do you get this data? How do you capture those insights that help to stop waste and gain savings? The first step is to install smart meters that can measure energy consumption with granular precision. Energy meters combined with a data-analysis platform will provide a detailed picture of a building’s energy consumption and help to capture those key insights that will reduce your carbon footprint and save money.
Take, for instance, a lighting system that isn’t shutting off when it’s supposed to — or a cooling unit that cycles when it should be idling, eating up more electricity than it needs. The right energy meter, together with the right data, will show you when those devices are not operating as they should and signal that it’s time for repairs or replacement.
Good metering solutions gather large amounts of energy data and analyze it in a way that provides clear and actionable insights. They can also send data to the cloud, where it can be integrated with other data sets to provide even deeper insights. Artificial intelligence (AI) can be applied to further analyze the data and offer highly accurate recommendations for saving energy in an automated way.
Energy savings made easy
With the right system, you can access your energy data in real time via an online dashboard. This data will not only help to optimize your energy usage, but it will also reveal how much energy you’re consuming at various rate tiers and how you can save.
For instance, an energy dashboard can show that energy consumption is down, but carbon emissions are up between 8 and 10 p.m. The reason is that your local utility is switching from solar to coal during those hours. The system might then recommend using battery storage to load-shift your energy consumption during that period, shrinking your carbon footprint. You’ll never be able to get this type of insight without granular data.
Here’s another example. As you know, energy rates are higher at certain times of the day. Now let’s say you have eight pieces of equipment running during those peak hours. Data can illustrate precisely which piece of equipment you can adjust to reduce usage. This way, you can be very prescriptive in how to change and phase your equipment to reduce your peaks and make a material impact on energy spending.
Every organization committed to sustainability needs to consider how data analytics can help drive not only better environmental outcomes but better economic ones. It’s a classic win-win — and it’s all in the data.