This past year was unusual in so many ways, but the bumpy ride we called 2020 also changed the utility industry for the better. As utilities automated more of their processes and data became easier to access, new challenges arose — and new solutions helped overcome those challenges.
Machines started taking over (in a good way)
Artificial intelligence (AI) took off in 2020. So did machine learning (ML) — ultimately helping utilities save time and money, and increase safety.
The challenge
Utilities are burdened with an overload of data that's inaccurate or inaccessible, decreasing the efficiency of their inspection processes.
The opportunity
ML algorithms can be trained to recognize issues during utility inspections. For instance, data engineers can annotate images of blown fuses and feed them into the model, so it learns how to identify this issue — even at odd angles or in dim light. The model continues to refine its abilities as more data is fed into it.
So, imagine that instead of looking at a thousand of a relative's vacation photos, you're only required to "ooh" and "aah" over the 10 that actually show the Eiffel Tower. In the same way, a utility can now use ML to cull through data and pull out only the most relevant images.
Data collection became (more) agnostic
With agnostic data collection, it doesn't matter how you get the data as long as it's accurate and high quality. But it does matter how you use it, and new aspects of this trend are helping utilities get the most out of their data.
The challenge
Utilities have always been using data-agnostic opportunities but may have been collecting through different modalities — from IoT devices to walks down the street to drones. And most of the time, those modalities are applied in specific departments instead of being used as an agnostic data source across the organization.
The opportunity
In 2020, we saw advancements in higher-level data management tools that aggregate data into one location, run it through standardized AI, ML, or manual analytical processes — and then use that information to provide a holistic view of your system.
In addition, thanks to more shareable data formats, every relevant department can now use data to make better decisions and take action.
Electric grids got a refresh
Smarter grids require smarter data collection and analysis. New and improved tech tools helped make this happen in 2020.
The challenge
47 states (plus DC) took a total of 446 actions related to grid modernization in Q1 2020, reports the N.C. Clean Energy Technology Center.
That's good news but modernizing the grid is tougher than it should be because asset inventories informed by manual inspection methods result in inaccurate data. Poles weren't where they were supposed to be. Some poles in the database didn't even exist.
Starting with this poor baseline made it difficult, as you can't modernize what you don't know you have.
The opportunity
A handful of technologies advanced in step to make it easier for utilities to know what assets they have and their condition — making modernization efforts faster and more successful.
For example, LiDAR improved (and became less expensive), enabling more accurate data collection. Adoption of drone inspections also increased, meaning utilities started to get better information, faster. And, as discussed, AI and ML platforms emerged to sift through data from various sources.
COVID-19 made workforce management more difficult
Efficient workforce management is key when you want to conduct just-in-time maintenance. It was already a complicated process and COVID-19 didn't help the situation in 2020.
The challenge
The pandemic made it a bad idea for groups of people to be in one workspace, decreasing the efficiency of data analyst teams. The lack of a way to prioritize data by importance slowed down analysts even more.
And when data analyst teams can't do their best job, this delays everything down the line, including sending out repair crews. At the same time, sending out crews for issues that may not be a priority increases the risk of exposing the workers to COVID-19.
The opportunity
In 2020, automated data analysis helped utilities "rack and stack" data so analysts could review images in order of importance. The least important data was reviewed by a machine, while human analysts focused only on relevant images.
With this advancement, data analysts could do their jobs faster and utilities were able to send repair crews to fix equipment with the most critical issues first. In other words, utilities could manage their workforce in a safer, more efficient way.
Next year promises to bring even more challenges and opportunities. As we learned from the coronavirus crisis, you can never predict what will happen. But you can make the most of whatever 2021 throws your way by adopting an "always ready" stance when it comes to new solutions and new technologies that can help turn challenges into opportunities.
