Big data is one of the buzz words tossed around in Smart Grid circles these days, and it will be the subject of discussion at a session I’ll host later this week. What exactly is it? Like the universe itself, Smart Grid data is in an expansion phase. Big Smart Grid data consists of large datasets enabled by the Smart Grid that create value for data owners and data users. (That’s my trial definition, and I invite your comments on it). This data is so new that it doesn’t have many examples of organization. However, other business sectors have been inspired by biology and organized data into taxonomies that create hierarchies, categories and rankings to identify relative positions of data within those
hierarchies. These experiences offer lessons to help utilities, service providers, and vendors manage the looming tsunami/avalanche/deluge of Smart Grid data.
Here’s my speculative description of how we will organize Smart Grid data (with apologies to my high school biology teacher). It will be categorized in various classes – such as utility data, consumer data, and market data. It will consist of primary data, completely factual “it is what it is” data; and secondary data created from primary data through analytics and business intelligence applications. Within the utility data class, there will be operational data, services data, and market data. Operational data includes status information about assets in substations, alarms, maintenance schedules, and meter readings. This operational data has subclasses of data, such as maintenance data, which will apply analytics to equipment performance data to predict and prioritize assets in need of repair.
Consumer data includes name, address, and meter number, and much more. For instance, a consumer may have a home energy management system (HEMS) service and voluntarily furnish household information about number of occupants, home office presence, heating, ventilation, and air conditioning (HVAC) details, and number and type of appliances. A significant amount of value is anticipated to reside in secondary consumer data –behavioral analytics of consumer usage data will have value to utilities, service providers, and vendors, in addition to the owners (consumers) of that data.
Utilities and other energy service providers need this type of consumer data to effectively enlist support for future energy efficiency and demand response campaigns and programs that reward changes in energy consumption. CRM and analytics applications can deliver valuable information to let utilities act as “trusted advisors” to consumers to reduce or shape energy use. The recent California Public Utility Commission (CPUC) proposed decision about consumer electricity usage data stated that “Enabling consumers and companies to assess and act on this information is key to advancing many of California’s energy policies, such as promoting conservation, reducing demand in response to grid events and price signals, reducing summer peak demands, and efficiently incorporating renewable energy and electric vehicles into grid operations.” It’s easy to hear the ka-ching of monetization in consumer electricity usage data, along with other classes of big Smart Grid data, and the critical need to effectively organize it into meaningful information.
Utilities and other energy service providers face dual challenges with big Smart Grid data – particularly regarding consumer data: 1) cost-effectively collecting consumer data with appropriate security and privacy safeguards and 2) climbing a steep learning curve to conduct analyses of the data and develop meaningful information. For many firms, it will be more effective to outsource the data collection and analytics to companies skilled at handling big data to get full information value from consumer data.
A lot of meaningful information will be exchanged in the next two weeks at several upcoming Smart Grid shows, including ConnectivityWeek , Grid ComForum, and the Smart Grid Technology Conference, and join me there to contribute to the conversations.