More and more, technology companies are realising “there’s gold in them thar data” … as in all the data that is being, or will be, generated by smart meters and other sensors deployed across the future smart grid.
Just look at all the data-related products and services targeting utilities: data aggregation, data mining, “big data” management, data analytics, business intelligence and more. All of them promise to help utilities make sense of the ever-growing volume of bits and bytes coming their way.
Now, a Florida startup says it aims make utilities even smarter by bringing yet more data into play. Delray Beach-based GridGlo asserts that data fusion is the way to go for utility firms looking to make smart-grid improvements pay off for them financially in even bigger ways.
The company claims its cloud-based, platform-as-a-service offering can provide “deep insight into energy behaviour patterns” by analysing not only data from advanced metering infrastructure (AMI) but a host of other sources, ranging from demographics and satellite imagery to financial data and social behaviour information.
GridGlo has used such disparate data sources to develop what it calls an “EPM” score (EPM stands for “Energy People Meter”). The EPM score, which can range from 1 to 1,000, provides utilities with “a real-time digital fingerprint of people’s energy behaviour patterns.” The score is calculated from data across four areas: behavioural (which includes location, activity and weather factors), demographic (a person’s gender, age, income, education and household composition), premise characteristics (type of building, windows, heating/cooling systems, appliances, etc.) and energy consumption (which includes data from meters, distributed energy sources, electric vehicles and home-area-network devices).
Using Google Earth-type information, for example, GridGlo can identify which homes in a utility’s service area have solar panels on their roofs.
That level of detail is sure to worry privacy advocates and some people who are already worried about what smart meters alone can say about their personal habits. However, GridGlo says it has a secure system that “de-identifies” data so developers who work with the company have access to only processed and anonymous, rather than raw, data about utility customers.
GridGlo is also exploring some other intriguing applications based on consumer energy data, including a demand forecasting tool that can work at the individual-premise level and a risk management tool that could help predict potential energy theft or consumer financial health problems.
(There’s another privacy concern-raising possibility: could utilities change how they charge or serve people based on the expectation that a customer might lose his or her job and income? It’s almost the energy equivalent of DNA analysis, where some worry that insurance companies or employers could discriminate against people based on their genetic makeup. GridGlo founder/CEO Isaias Sudit hits it right on the head when he calls this current time in smart-grid development “a market of great latent potential nearing an inflection point.”)