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Analytics and the next generation of utilities

By Steve Cavolick 

The utilities industry is known for maturity, stability, and its conservative nature. Strategic initiatives in the past have focused on meeting regulatory policies, customer service, and capital investments in distribution lines, substations, transformers and automatic switches. While never thought of as a strategic differentiator, data and information management were used primarily to make operational decisions around transmission, maintenance, and customer billing.

That thinking shifted about 10 years ago with the deployment of Advanced Metering Infrastructures (AMI) and smart meters. Instead of monthly measurements of power consumption, these meters could deliver data from every customer every 15 minutes, 24 hours a day. Suddenly, power companies were faced with challenges around the movement, cleansing, storage, and analysis of massive amounts of data.

The industry continues to be rocked with changing conditions and new pressures. Power demand is increasing, and climate change is forcing companies to mitigate their carbon footprint. Below are just a few of the new opportunities that also demand the mastering of data:

Growth of Electric Vehicles (EVs)

Some states have seen 100% increases in EV sales year over year, and other states, like Oregon, have passed regulations that compel utility companies to make infrastructure changes that keep pace with EV adoption. Knowing EV penetration throughout the customer base and how that effects demand will be critical to understanding where to invest in charging depots and where to shore up infrastructure based on the new demand. Analytics can also be used to change customer behavior to charge vehicles in off-peak hours so that the demand curve can be flattened throughout the day. While EVs will cause capital investments for utility companies, the concept of sharing power via Vehicle-To-Grid (V2G) technology also represents a opportunity for companies to provide grid services, although this has not yet been commercialized.

Increase in Distributed Energy Resources (DER)

Examples of DERs include home solar panels, EV charging stations, and batteries. These give customers more control over electricity generation and consumption, which represents competition for the utilities that have gone mostly unchallenged in their entire existence. Advanced analytics and predictive models will show where adoption is likely to happen, as well as the operational and financial impacts of the new resources. IoT data from utility- and customer-owned resources everywhere in the grid will drive new efficiencies, cut out overspending, and drive new revenues from DER marketplaces.

Prevent Fraud and Loss

Today, observing usage patterns at the individual customer level relies heavily on human intervention to validate what is reported. Predictive algorithms can automate processes to identify usage that might be involve theft and minimize the human element while increasing the amount of revenue that could be recovered. 

Using data from traditional sources as well as newer devices and applications will allow utilities to better understand customer usage patterns, manage energy constraints, and reduce fraud- all while improving compliance with regulatory requests. Those companies that can manage this avalanche of data increase the likelihood of meeting the needs of internal and external customers, and delivering expected performance to consumers, regulators, and stockholders. If your company is not an expert in the areas of managing and democratizing data, we are. Let us help.

The LRS Big Data and Analytics team has over 20 years of experience in statistics, information management, predictive analytics and AI, and data warehousing. If you are interested in understanding how we can help you find value in your data with advanced analytics, please fill out the form below to request a meeting.


About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.