Waiting for your power bill to understand and manage energy use in production facilities doesn’t cut it these days. This passive approach gives you an incomplete picture of how and when you’re using energy in your manufacturing operations.
More than that, data from a bill likely isn’t tied to what’s happening in production. You don’t know when you’re consuming energy to produce good product, bad product, or even no product at all.
Active energy management approaches use real-time data associated with what’s happening in production to identify where you’re wasting energy—and money. This can drive down energy usage and costs and position your operations to meet growing pressures to improve sustainability, comply with new environmental regulations and one day achieve net-zero emissions.
Key opportunities from active energy management
Real-time data enables active energy management, and you likely don’t need expensive metering to get this information. Odds are good the data are already in your operations—if you know where to look.
That’s where innovations like energy objects play a role. These freely available pieces of code give you access to energy data from power hardware like drives, overloads, and circuit breakers so you can see how energy is being consumed in production. Essentially, you turn existing hardware into new meters, making investments and the pathway to active energy management more manageable.
Energy management software allows you to create a baseline of what your normal energy usage looks like. Then, you can use the software to continuously monitor how you’re consuming power in relation to what’s happening in your operations. That’s how you uncover where you’re spending the most energy or potentially wasting it.
For example, one manufacturer discovered it was running its machines at full capacity when it wasn’t producing good product. By switching machines into low-power mode in these instances, the company reduced energy costs by about 70%.
In some cases, having the data is just the start. By layering on additional technologies like artificial intelligence (AI) and machine learning (ML), you can further improve production performance and energy usage.
One water/wastewater treatment plant in the U.S. was looking to improve efficiency and conserve energy. A key issue was that operators had to manually reset setpoints when conditions changed. The solution? An AI-enabled control system that continuously monitors and learns the current state of operations and adjusts controls as conditions change. The plant cut power use by 2,200 kilowatt hours per day and produced $100,000 in savings in chemical and energy usage.
Where to start: Learning what’s already on your shop floor
A good place to begin your journey to active energy management is understanding your existing infrastructure. Assess your machinery, sensors, and meters to identify what data they produce and how this data can support your energy management needs.
An assessment may reveal, for example, gaps in your network infrastructure that need to be closed so you can seamlessly send energy data from the edge to the cloud. When you identify a gap that needs to be filled, whether it’s in your hardware, software, or network, think about how potential solutions to bridge that gap will benefit your overall approach to energy management.
For example, how can it reduce complexity to minimize burdens on staff? Ideally, active energy management doesn’t require you to hire new specialists in areas like data science or sustainability. Rather, it’s just another aspect of production that your existing specialists can manage.
Energy management software should be able to read, store, and analyze energy data and then present it in a meaningful way for users at your company. The software also should align to standards such as ISO 50001 and have built-in reporting capabilities to reduce your compliance efforts. And you should be able to deploy it where you want—at the edge, on-premises, or in the cloud.
Also, as you shift to an active energy management approach, remember that productivity and sustainability work hand in hand. Improving one can improve the other.
That was the case for a tire manufacturer that used an ML model to address out-of-tolerance tire splices that were hurting productivity and energy efficiency. The model predicted when bad splices would occur and prescribed changes to manipulate them back into tolerance. This reduced stoppages by 45%, allowing the manufacturer to produce an additional 600,000 tires per year while reducing overall energy consumed per tire.
Where to gain the visibility you need
You can’t go after energy-related problem areas in your operations, from energy spikes that indicate something is wrong to energy-intensive processes that can be run more efficiently, if you don’t have the data to show when and where problems are happening.
By shifting to active energy management—using your existing infrastructure, with minimal changes—you can get the data you need to uncover these problem areas so you can make improvements that benefit your operations, the environment, and your bottom line.