Reducing waste and responsible energy management has become a necessity for corporations looking to shore up their image in the eye of the public. However, while going green is certainly a commendable, ethical goal, it's also a fiscally responsible one. Companies that implement green strategies – such as waste reduction, energy efficiency and predictive maintenance – invariably save money in the long run.
An effective energy management strategy requires the latest technology. Today, that's a combination of the internet of things (IoT) and machine-learning algorithms, more commonly known as artificial intelligence (AI). IoT solutions can be implemented as narrowly as at the circuit level, and by leveraging and analyzing that data with AI, decision-makers can pull actionable information to significantly reduce waste and further optimize business operations. AI also enables real-time alerts and notifications as well as the automation of key functions, such as climate control and lighting. [Check out these creative ways businesses are using AI in their day-to-day operations.]
The internet of things delivers data-driven insights
The power of IoT comes from the granular data it provides. Installing sensors on your existing devices enables them to communicate information about conditions like energy usage, pressure, temperature, uptime and so on across your computer network.
"What sets IoT apart from other communications technologies is the way this global network of physical objects automatically collects and communicates data from the real world," said Safi Oranski, head of IoT for IoT energy management company Panoramic Power.
As data rolls in, your organization gains both a historical and real-time view of how your systems operate. For example, manufacturers can monitor the operation of individual devices and keep an eye out for anomalous behavior that might signal an impending problem. Early access to this information makes predictive and preventative maintenance possible, before a problem impacts productivity.
Contextualizing data with artificial intelligence
Implementing IoT is only half the battle, though. With such an immense amount of data flowing in, it's impossible for human operators to efficiently analyze it all. That's where machine learning comes in.
"[For] every device connected to the system, the [machine learning] algorithms start watching it for a couple weeks and learn that behavior," Oranski said. "It registers specific key performance indicators relevant to the device, things like working hours, idle hours, off duty, starts stops."
Based on data from the connected devices, the algorithms then form a picture of what general functional use looks like. Anything that strays too far from that range is flagged as potentially problematic and in need of a closer look by humans.
By examining other contextualized data flowing in from the IoT sensors, AI can also analyze things like pressure changes over time, energy usage, output and so on. Comparing, say, a spike in energy usage along with an increase in pressure can help human operators better identify the specific problem quickly and then engage in preventative maintenance or replace the device entirely.
The bottom line
IoT and machine learning algorithms have availed organizations of information they never had before, contextualized in a way that doesn't just support decision making but facilitates it.
"I believe all major companies will ultimately deploy IoT in some way at some point in the next few years," Oranski told Business News Daily. "The technology is available at a reasonable cost with proven ROIs."
Taken to this level, going green isn't just an ecological consideration or a marketing boon, but a financial imperative that businesses require to remain competitive. As IoT and AI adoption grows, companies that don't get on board might well be left behind as their competitors save money, reinvest in other areas and boost their profit margins.
In the future, Oranski said, it is likely that IoT and machine learning will grow beyond simple updates and recommendations, and reach a point where they can automate sophisticated, system-level processes.
"These are often things that change based on circumstance," Oranski said. "[These systems] alert people of opportunities to save or do things differently, but they actually need to make those decisions. But the more that people trust machines and algorithms, the more they will be inclined to let them make their own decisions."