IoT and machine learning in utilities: What's the difference?

IoT and machine learning are some of the biggest key words in the incoming tech sphere, and their potential for the utilities industry is huge. So how exactly are they defined? How can they benefit utilities and how can they work together?   

Defining IoT

The Internet of Things, better known as IoT, is a system by which objects connected via the internet can produce, exchange and react to data. These objects range from cups to cars, components of a solar panel to whole devices like phones. Items are embedded with a computer system that often includes a variety of data producing sensors. All together these objects combine to form a network of devices that transfer and react to data without the need for any human interaction.

In short everything is connected. So your alarm clock could notify your kettle when you wake up and prompt the kettle to boil your morning tea. The possibilities for increasing efficiency are almost endless.

Defining machine learning

Machine learning is a form of artificial intelligence (AI) where machine systems are given the ability to "learn" from data and experience without being explicitly programmed to do so. The objective is to allow computers to improve a system or performance of a task over time without any human intervention.

This is slightly different to the concept of AI, which is the much broader concept of computers possessing cognitive functions such as learning and problem solving. Machine learning is to date our current application of AI and demonstrates the learning function. It is often most useful as a tool for data analytics. Machine learning is able to find patterns in huge quantities of data and deliver insights without being explicitly directed where to look.

What are the benefits of IoT in the utilities sector?

IoT is currently being adopted by industries across all sectors, but what does its interconnected capacity have to offer utilities?

• Smart meters and data accumulation: Some of the most useful IoT devices for the utilities industry are smart meters. Automatic Meter Reading (ARM) processes, allowed by smart meters, are already being used extensively within many organisations to great success, allowing resource efficiency and cost savings. Using IoT devices in things like the power grid will allow utilities companies the ability to monitor previously unknowable quantities such as second-by-second demand and use rates.
  
  
• Cost savings and increasing efficiency: Monitoring minutiae through IoT devices in machinery or the electricity grid means that faults can be dealt with immediately on a smaller scale before they build into major outages. For instance, IoT devices monitoring home utilities detect non-bill payers immediately, saving utilities companies funds. These devices are also able to monitor utility usage and ensure that supplies exactly match demand, creating little to no waste.
  
  
• Stronger customer relationships: Closer monitoring of customer usage and the reliability of their utility supplies helps companies to better understand customer needs and meet their demands. Home IoT devices can also help customers personalise their utility usage through monitoring their own consumption and costs.

What are the benefits of machine learning in the utilities sector?

Machine learning shines when it is strategically applied and can analyse and learn from incoming data - of which the utilities industry has plenty:

• Asset optimisation: Machine learning's ability to interpret huge amounts of data and predict outcomes based on incoming information makes it perfect for forecasting. Algorithms that can analyse data using past experience alongside incorporated industry knowledge is the most efficient way to determine system failure probabilities and so allot your repair time and funds accordingly.

• Managing outages: Machine learning is the tool behind the "self-healing grid". This is where analytics models are able to predict outages about to happen by responding to data patterns. They can then either alert the necessary resources for repair or even divert power through an alternative route to keep the system up and running. 
• Improve data driven decision making: Data driven computer learning enables whole systems to be better maintained. By analysing collated data regarding grid performance, machine learning is able to disassemble processes and highlight which areas need the most attention in the near-term. It can also allow for a more in-depth understanding of customer behaviour and demand allowing for better engagement through a more tailored service.

How do they work together?

IoT and machine learning play unique but ultimately connected roles in the improvement of utilities. 

Functionally, one collects and collates data, the other is the "mind" behind it, which makes use of the drastic increase in information to produce useful insights. Without machine learning the colossal amount of readings and real-time monitoring data produced by IoT could be overwhelming rather than useful. Similarly, without the data produced by IoT, machine learning is unable to access information about key components of utilities function, limiting the scope of its analysis.

Principally, IoT and machine learning are a partnership that combine with analytics solutions to create a more optimised and self-sustaining utilities function.

If you're looking to understand more about how IoT and machine learning could improve your utilities operations, get in contact with the experts at SAS for more information.