Oct 22 2021
Data Analytics

Three Ways Utilities Can Use AI to Boost Operations

Utilities have not yet optimized the potential of artificial intelligence for unleashing the power and value of the data they collect.

Organizations are clamoring to be able to use all of the data that’s being collected through digital tools. For utilities, this can be a particularly daunting challenge due to the industry’s reliance on equipment, mobile devices and the Internet of Things. All this data can have a major influence over the success of organizations, but only if it’s used in the right way.

This is where artificial intelligence has had a big impact in other industries. Financial services have used it to craft personalized experiences for customers, retailers have used it to help determine when to restock, and nonprofits have used it to optimize fundraising. The energy and utilities sector can similarly harness the power of AI to streamline operations and optimize output.

Through predictive analytics, AI can be used by the industry to manage resources, store energy and increase efficiency. Automating the collection, storage and management of data will make the industry more efficient and profitable.

Predictive Analytics Can Forecast Energy Needs

According to Arun Majumdar, Jay Precourt Provostial Chair Professor at Stanford University and a member of the department of mechanical engineering, “​​If you step back for a moment, you realize there are two separate trillion-dollar industries — the energy industry and the data and information industry — which are now intersecting in a way they never have before.”

The ​​Electric Power Research Institute is working with utilities and the AI community to release data sets for developing and training models. These data sets can be used to increase efficiency, enhance predictive modeling and enable more effective identification of damage to equipment that may need to be repaired or replaced.

Microsoft has also developed a means of helping power companies enhance their predictive analytics through Power BI, a suite of business analytics tools that the company says can “improve and transform the way sustainable energy is managed and generate value across energy production, supply, distribution, and consumption using data-driven insights.”

Data analytics tools such as Power BI can be used by utilities to predict and plan for future customer demand. Microsoft says its product can “transform reactive decisions to predictive and preventive strategies with enhanced critical equipment and resource management in energy production and distribution channels.”

AI Can Help Drive Resource Management Within the Energy Sector

Majumdar spoke at an AI and Electric Power Roundtable hosted by the Electric Power Research Institute earlier this year. “The people who focus on data do not generally have expertise regarding the electricity industry, and vice versa. We have entities like EPRI trying to connect the two, and this is of enormous value.”

When it comes to resource management, the industry publication POWER reports that “EPRI is developing models and tools which will enable operators to enhance their responsiveness and flexibility to utility grid signals in the most cost-effective way. Coupled with the digitization of building control systems, AI predictive models will provide utilities and customers greater affordability, resiliency, environmental performance, and reliability.”

MORE FOR UTILITIES: Learn how to protect your IoT environments with IAM.

Another example of this AI capability was announced in November 2019, when Baker Hughes, C3.ai and Microsoft went public with an alliance they said will make it easier for customers to adopt scalable AI solutions run on Microsoft Azure. According to a Microsoft statement released at the time, “As a result, energy businesses will have a secure and reliable suite of enterprise-scale AI applications optimized to run on Azure. These solutions are tailored to address challenges across the entire value chain, from inventory optimization and energy management to predictive maintenance and process and equipment reliability.”

AI-Powered Energy Storage Can Increase Efficiency

According to EPRI, current new-energy storage systems “are typically 4-hour duration or less, corresponding to peaking capacity and ancillary services needs. However, in the coming years as storage is deployed to replace higher capacity factor conventional generation, absorb longer periods of renewable overgeneration, and support resilience during severe weather events there is a potential need for longer duration storage.”

Utility Dive reports that the Department of Energy is looking to AI and machine learning to accelerate research for long-duration energy storage. At the DOE’s Long Duration Storage Shot Summit on Sept. 23, Deputy Energy Secretary David Turk said bringing long-duration storage to the grid wouldn't just make it possible to rely on more renewable energy, but also "increase resilience and lower energy burdens" for vulnerable communities.

According to Utility Dive, the Rapid Operational Validation Initiative, or ROVI — the proposed initiative from DOE's national labs — seeks to close the information gap for electricity providers “by using machine learning and artificial intelligence to model performance of different long-duration storage technologies, including predicting how the technology will lose performance or hold up physically over time. The initiative would rely on industry data and digital twins of the storage systems to model the long-term performance.”

ipopba/Getty images

Learn from Your Peers

What can you glean about security from other IT pros? Check out new CDW research and insight from our experts.