Smart meters, consumer choice, and prosumer energy production are creating an increasingly complex energy grid, disrupting traditional utilities markets, and creating new opportunities for utility businesses.
To meet rapidly changing customer expectations and move forward their digital transformation strategies, utilities are working to leverage smart meter data by building out their Edge and IoT infrastructure, and adding distributed AI analytical capabilities to support a broad range of business goals with new insights. These goals include improving efficiency and reliability, increasing safety, delivering affordable services, and ultimately – advancing customer satisfaction. Leaders are also using IoT data and distributed analytics to innovate – creating new services and opportunities to diversify their business models.
Traditionally, organizations have maintained a data lake and applied machine learning on consolidated data sets. This methodology requires moving all data to one location for analysis – but is no longer efficient with the increased volume and velocity of smart meter data. Instead, utilities are building a distributed AI capability where data isn’t relocated back to a central location each time it needs to be processed. Instead, AI at the Edge (so all data isn’t required to be transmitted) is combined with machine learning at the core. Distributed analytics reduces time-to-insight, delivers a powerful continuous learning capability, and provides previously impossible opportunities to identify anomalies and potential threats.
One important foundational element is a flexible, but homogenous, IoT architecture that can integrate with multi-cloud platforms as workloads dictate. Edge compute and gateways can capture high-velocity data and are critical components enabling distributed analytics in both online and offline environments. This integration is important as computational workloads can now run in a variety of different configurations – whether in a public cloud, private cloud, on-premises, or at the Edge.
Modernized IoT networks that leverage distributed analytics are helping utility leaders address a range of high-priority business needs. For example:
- Enable (real-time) demand forecasting to reduce costs and drive affordable service
- Predict problems, deploy resources efficiently, and avoid outages to improve reliability
- Modernize physical security to keep facilities and employees’ safe
- Automate operations, enabling new service development, and driving customer satisfaction
Utilities are enabling more proactive maintenance with computer vision technology (cameras with built-in analytics and machine learning capabilities) on drones that inspect assets and monitor facilities. Or, for example, monitoring vegetation overgrowth and flagging encroachment around power lines, minimizing the threat of outages. Some states are even using drone data and distributed analytics to help predict where wildfires are most likely to start – to pre-emptively shore up infrastructure security where needed.
With better insights from optimized distributed analytics, utilities can build smart grid capabilities that track energy transactions, improve security, enable efficient resource allocation, and deliver new business models and revenue opportunities.
To learn more about empowering your organization to take full advantage of your data resources with IoT infrastructure and distributed analytics, read: “Utilities at the Edge: Intelligent Management through Strategic Modernization.”
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