Data Analytics in Utilities: What’s Possible?

March 9, 2021 • Shannon Flynn


Data analytics platforms can process huge amounts of information in short periods. These resources help companies make smarter, more informed decisions to keep operations running smoothly. Here’s a look at what’s possible for data analytics in utilities. 

Increasing Awareness and Preparedness

Many everyday people don’t think much about their power providers unless things go wrong. Experts have long warned that the effects of climate change could put a strain on numerous sectors, including the utilities industry. Seasonal storms could also wreak havoc on resource availability.

However, tech startups claim to have data-based solutions. For example, a company called Overstory proves satellite images of forestry data. Then, teams understand where to go to deal with dying trees or overhanging limbs that could spark future fires. 

Myst AI is another option. It uses several machine learning techniques to give utilities providers information they may otherwise miss, including market data and customer load information. 

It’s not possible to predict the future with certainty. However, data analytics tools can make utility professionals more aware of what’s likely to happen, helping them react accordingly. 

Improving Efficiency and Operational Success Rates

Depending on data analytics in utilities also helps providers stay competitive in an ever-challenging market. Imagine a scenario where a company receives customers’ meter data every 15 minutes instead of monthly. Leaders at that business would then have a much clearer idea of profits. 

Analyzing data can also allow providers to get warnings of impending equipment failures. If sensors collect data about a piece of machinery’s temperature or amount of vibration, technicians can see the signs of something amiss before the problem causes a service disruption. 

Similarly, if a company experiences repeated breakdowns of essential equipment, representatives could use data analysis to get to the root causes of those performance issues. Then, they save money and time while addressing them. 

Strategically digging into historical information increases customer satisfaction, too. Finding out which questions people most commonly ask could prompt a company to update its help database or decide to offer new services based on people’s most frequent requests. 

Preventing Tree-Related Power Outages

Besides potentially impact safe road travel, downed trees can cause power outages. Anyone who has ever lived in an area with frequent high winds or winter ice storms can probably recall seeing the trucks full of crews sent to deal with the fallen trees and broken lines. 

The usual approach to dealing with the problem is to periodically send teams to deal with the tree growth closest to power lines. The hope is that better maintenance reduces the likelihood of later problems. 

However, this is a costly and time-consuming approach. Utility company professionals usually plan rotating schedules for different service areas, but that means it may take years to reach all the places with electricity infrastructure.  

Researchers realized they could improve the process with an intelligent data model. It finds the most vulnerable utility assets, then presents a map so that maintenance professionals know where to visit first. This approach also predicts weather hazards and reveals the economic impact of service outages in particular areas. 

Unplanned disruptions become significantly expensive for utilities companies, not to mention frustrating for the customers that rely on their services. This technological innovation could improve outcomes for everyone involved. 

Determining the Impact of Decisions on Vulnerable Populations

Another compelling use of data analytics in utilities relates to assessing how certain decisions affect potentially disadvantaged population groups. Many people know that utilities companies may offer customer-facing interactive interfaces that show usage patterns and help people reduce their bills.

However, providers also unveil billing strategies that are outside of an individual’s control. One of them that’s becoming more widespread is called demand-based billing. It means that people pay more for electricity if using it during peak times. The aim is to incentivize customers to adjust their patterns and become less dependent on power when the highest percentage of people need it. 

A study showed that this pricing structure has disproportionately negative impacts on some members of society, such as older people and individuals with disabilities. Researchers looked at data from more than 7,400 customers and created six vulnerability indicators that could apply to them. 

The findings showed that a time-of-use approach to billing increased costs for everyone but especially elderly and disabled customers. Moreover, setting this kind of payment structure led to worsened health outcomes for Hispanic people and people with disabilities. 

Conducting research like this could encourage utility professionals to think about a new pricing structure’s full impacts. Making a change could seem like a good idea at first, but it could cause hidden, long-term damage that adversely affects the quality of life for some customers already dealing with additional hardships. 

Data Analytics in Utilities Shows Potential 

Opportunities to apply data analytics in utilities are still in the relatively early stages. However, as more companies opt to do it and get promising results, others will follow suit. If these examples are any indication, it may soon become apparent that utilities businesses that decide not to use analytics tools are at distinct disadvantages compared to peers.