Recent data revealed that there are about 64.7 million smart meters in the U.S. measuring and recording electricity usage. Globally, the total number is likely to reach close to 800 million by 2020. Thus, the real-time flow of big data in energy sectors presents a challenge to utilities worldwide.
Utility data collection has transformed from a small army of meter readers manually gathering data on a monthly basis to near-constant two-way communications provided by smart meters. Every 15 minutes, real-time data is being shared between consumers and energy providers. A large utility may have anywhere from 2 to 5 million customers—which gives some insight into the magnitude of the data being collected.
What promise does this flood of data hold for consumers and utility providers? How can both sides benefit from the insights gained from big data in energy consumption and generation?
In the not-so-distant past, consumers cared little about where or how their electricity was generated. They simply wanted it for the lowest available cost. But energy production and climate change concerns have altered how consumers view their consumption. Utility customers have become more engaged in how they consume electricity, how it’s generated, the sources it comes from, whether renewable resources are used, and if it will reduce their carbon footprint.
For utilities, this is a dynamic to which most have not yet adapted. Many wonder how they can use smart meter data to give value back to customers who want more control of their own energy consumption as well as ways to reduce their energy costs. Many also aspire to use real-time data to more effectively communicate the kinds of offers their customers want, especially as utilities pursue new revenue models, such as energy-efficient appliances that diversify their revenue streams.
Smart meter data is the pulse of a utility’s customer base. However, the act of collecting data in 15-minute intervals provides limited value if it’s not processed and analyzed in a cost-effective, timely manner. The utility must be able to take gathered data and build predictive models for real-time use cases like outage prediction and prevention, demand forecasting, and theft detection.
Utilities often seek targeted point solutions—apps that address very specific challenges. The problem with this approach is that it creates even more information silos. The apps solve specific issues but don’t address how to manage the overall data pool. In many cases, they don’t adequately enable utilities to integrate their internal data sets or incorporate how to work with external data from public sources, social media tracking, or weather information to enrich data science models.
Energy companies have come to realize that adopting a data-centric approach provides a foundation for their big data and advanced analytics programs, enabling them to deliver a variety of applications across multiple lines of business by leveraging similar data sets. In addition, they’re also able to store larger data volumes more effectively and process data sets more quickly.
Utility companies must leverage the value of their data sets to empower business growth. Consider these examples of other companies that are taking advantage of the insights gained from big data in energy consumption and generation.
If your company deals with large volumes of data, it may be time to switch to stream processing in order to make the most of your analytics.