Smart technology is uncovering a wealth of new, rich data at a volume never witnessed before. While the sheer volume can be overwhelming, the new data offers utilities a number of opportunities, explains Bradley Williams, vice president of Oracle Utilities Global Business Unit's Product Management.
These are guaranteed to enhance operational efficiency and reliability, improve customer service and improve customer relationships.
The smart meter allows utilities to collect outage, interval, voltage, tamper event and diagnostic flag data. This data helps utilities to identify and correct outages more efficiently, provide customers with consumption information, identify power theft, and aggregate usage to assess asset risk and asset replacement issues.
The influx of data doesn’t only originate from smart meters-it can also come from other sources. For instance, customer feedback can come from various communications channels, including social media channels.
There are also different types of data-alternative distributed generation data, weather and forecasting data, data generated by sophisticated sensors, controls and grid-healing elements all along the utility grid.
Revealing new value
Big data is forcing utilities to re-examine how they look at information strategy, operational structure and customer engagement. Utilities are also having to re-examine their ability as an enterprise to adapt to the change. Data flow is being absorbed enterprise-wide instead of organizational silos which gives decision-makers access to a wider range of information.
As operational technology within the grid has increased and matured, there has been an evolution in the amount of data being collected and in the expectations of analytics capabilities. The electric industry is moving towards clearer analysis. They are taking more available data and analysing it at a faster rate.
Through pointed analysis, utilities are able to draw insights from real-time information streams. With historical and real-time data at hand, utilities can plan better with predictive analytics. Big data and predictive analytics are being described as transformative in nature; twin pillars of the new utility.
Utilities can now look forward to being more proactive and potential operational problems can be mitigated before they occur.
Different analytic needs and approaches
Each utility's specific data analytics needs and approaches are as individual as the utility itself. There is therefore no one simple solution or answer to data analytics.
Big data and business intelligence dashboards cannot provide single answers. Utilities still need operational reporting. They also need to know their key performance indicators (KPI), such as their system average interruption duration index (SAIDI), their system average interruption frequency index (SAIFI), and much more.
To manage those needs, operational data analytics can provide a historic and predictive real-time view of the utility's operations. This can help a utility in its reporting requirements, as well as its ability to handle outages more efficiently and effectively.
With predictive analytics, a utility can begin to compare historical data to identify trends, mash-up weather forecasts and forecast demand to more accurately predict energy usage and grid impact of renewable generation. In addition, they can turn reactive outage management into proactive outage management by monitoring transformer and other asset health rather instead of using “run-to-failure” asset management.
Customer data analytics can help the utility provide customers with information about their usage patterns, target them for new programs, establish pricing programs, implement more effective demand response programs and alert customers to usage spikes that may indicate appliance issues.
Opportunities into capabilities
There is great value in sharing and leveraging diverse sources of data across the utility enterprise. To do that, however, a utility must assess its current business processes before it draws on the new data for answers. Decision-makers need to ask “What else needs to be done?” and “How best can we achieve this value?”
To achieve the full value of analytics, utilities need to draw from predictive analytics, they will need to share data and collaborate across enterprise silos, and experiment with mash-ups of the disparate types of data collected. The utilities’ potential questions are unlimited and, once identified, can provide increasingly stronger business cases for the utility to pursue.
Cloud analytics-the possibilities
The possibilities for cloud computing and cloud analytics within the utility sector is gaining interest. Recent Oracle Utilities research in how North American electric utilities are using the increasing volumes of smart grid data, shows substantial interest in cloud-based solutions for data management and analysis, and utility implementations for specific business cases are growing in number.
Cloud-based solutions also help utilities resolve the data analytics skills gap. This can be a major obstacle for companies wanting to move ahead with clearly defined data analytics business cases. Often, utilities struggle to find staff who are skilled in big data analytics and electric utility business knowledge. Skilled electric utility staff are too busy to learn new analytics tools. Data scientists and analysts don't know the utility operations well enough to know what to look for.
This is where pre-packaged, cloud-hosted analytics applications offer utilities solutions to these issues without the capital investment in more data servers. As part of the service, they are assigned utility skilled data scientists.
In addition to this, cloud-based solutions are known to improve the speed, security and scalability of data management systems.
While cloud is still in the early stages of adoption within the utilities industry, cloud-based analytics will allow utilities to collaborate with other utilities to address complex business issues. Cloud can be used as a testing site to share analytical information and analytics best practices.
This will allow the utility analytics practice to stay fluid, rather than static, says Mr Williams.
Today’s utility faces a major challenge: the ability to translate “big data” into actionable intelligence. This data must be used to make decisions that will enhance business performance, service reliability and customer engagement.