This is part six in our series designed to help businesses gain efficiencies, cut unnecessary costs, and protect their bottom line in today’s uncertain and complex landscape. The Kepler platform addresses a vast array of Machine Learning (ML) use cases for the Energy and Utilities industries, and its easy-to-use Automated Data Science Workflows ensure all leaders in the Energy and Utilities sector — and their teams — can leverage the host of benefits of ML-powered insights quickly and effectively. Below, we outline how to leverage AI for Energy and Utilities with Kepler’s advanced capabilities to overcome the range of challenges putting businesses like yours at risk, today.
COVID-19 has impacted industries in various ways, from forced store closures and supply chain disruptions that pervade the retail, CPG and manufacturing sectors, to soaring network demands for telecommunications. Energy and Utilities leaders are encountering their own set of challenges, despite the fact that the Energy and Utilities industry is one of the most equipped with continuity plans to deal with various crises.
According to a report by PwC, 75% of enterprise leaders across multiple verticals are most worried about the financial impact on their organizations, including operations, future periods, capital resources and liquidity. There are also worries about workforce reductions and productivity, which 41% of leaders cite worries about. And, crucially, 20% are concerned with not having enough information to make good decisions as they navigate the murky waters of this crisis.
Hydroelectric utilities organizations across the globe are responding in various ways to help consumers and protect their workers right now. For example, the Toronto Hydro organization is allowing flexible payment options to their customers, and is focusing only on critical work on the grid. Likewise, BC launched a bill forgiveness program for three months to help small businesses and customers weather the storm.
In the United States, electricity usage was 3% lower than the previous year, and experts say that the utilities revenues will significantly suffer, despite the uptick in at-home use. This is partly due to the fact that rate increases are halted and shut-offs are more unlikely. But, the biggest dip in revenue is owed to dwindled business electricity demand, which makes up about 60% of total demand across North America, according to VentureBeat.
Prior to COVID-19, AI activities in the Energy and Utilities sector may have been reserved for innovation projects and various proofs of concepts. However, the pandemic has accelerated the need for leaders to focus on prioritizing ML that will address business-critical use cases focused on boosting efficiencies, assessing risks, automating what’s necessary, and protecting the business’ bottom line.
A recent report by Deloitte lays out some of these key challenges in more detail, highlighting what to expect in the short to medium term. From maintaining a healthy and effective workforce to ensuring that all customers get access to what they need, the road ahead is full of challenges. Deloitte predicts leaders will experience obstacles, such as:
While the road ahead may look bumpy, there are a host of ways that Machine Learning can significantly optimize processes within Energy and Utilities organizations. The bright side? When the next normal comes around, these newfound efficiencies can have a significant long-term impact on the health of your business.
Below, we outline how Kepler’s Automated Data Science Workflows can help leaders in Energy and Utilities, can leverage advanced ML to solve complex problems and key use cases with the team they already have. Kepler’s intuitive workflows were designed to alleviate the talent gap, ensuring SMEs and analysts on your team can leverage the power of ML, with no previous experience required.
Kepler’s Anomaly Detection workflow empowers organizations with the ability to tease out irregularities in datasets to resolve possible issues in advance, which is called predictive maintenance. For your teams seeking to better understand grid areas that need work and maintenance, anomaly detection can easily identify points of concern that need attention. Not only is this key for your maintenance teams, preventing residential outages is more crucial than ever now that so many customers are confined to their homes. Consider predictive maintenance as a necessary safeguard against surprises, and an insurance policy catered toward ensuring your grid is maintained effectively.
With Kepler’s Regression workflow, organizations can better plan for the future and know what’s ahead by drawing connections between variables, and anticipating future trends. With regression, you can make accurate predictions on pricing, and estimate future revenues by using a wealth of data types — from usage, demographics, historical and more — to better understand accurate energy pricing at scale, whether residential or commercial
Kepler’s Text Classification workflow allows organizations to automate back office paperwork by classifying key types of information and classifying their categories. With this workflow, organizations can easily manage incoming communications and prioritize accordingly. This will allow your customer service team to address key points of concerns for customers quickly and effectively, and tackle high-priority needs first.
Want to learn how Kepler can help your Energy + Utilities organization?
Download our industry infosheet here.
