Smart meter data: Enormous potential yet unexplored?

Now when smart meters are able to emit energy data Flutura came up with 6 ways to unlock the value from smart meter data.

As mentioned in our previous post, digitization of electricity meters (smart meters) has opened up plethora of value opportunities in utilities domain. The Utility sector is ripe for unlocking energy efficiencies by understanding energy consumption habit patterns at a level of granularity which was previously not possible - neighbourhood & consumer level. It also offers an opportunity for reducing technical and commercial losses along the complete grid value chain. Flutura has identified seven ways by which utilities can unlock value in the last mile of energy distribution

Pinpoint grassroots level neighborhood guzzlers

With Smart grid data one can have granular energy consumption patterns in an hourly on 15 min

interval time frame. One can do micro segmentation of consumers based on amount of power

consumed, their deviation from baseline consumption, consumer type and location.

Time of use pricing

Peak power tariff for industrial units would be different from

peak power tariff for hospitals, government entities. Another

dimension which can be brought in tariff for individual

households who have a 2 sigma variance from neighborhood

baselines can be higher.

Signature extraction & Habit design

These energy intensive appliances can be put on a watch list and their consumption signatures

can be detected. This consists of analysing changes in the voltage and current going into a house

from the smart meter time series data and inferring what appliances and specific individual

energy consumption.

Predictive models for preventative outage hotspots

Decode patterns leading up to an outage event - brownout frequencies, transient voltage, and

step change in energy consumption.

Next Best Distribution transformers interventions

Current harmonics from Smart meter data can be used to identify ageing of transformer caused

by harmonics due to non-linear loads. One can look at power harmonics data, brown outs,

blackouts and transient event data to rank order and prioritize DTRs in specific neighbourhood

which need to replace.

Reduce spot buying through bottom up forecasting

With the availability of granular data, neighbourhood level energy profiles can be created based

on individual smart meter data and then used to triangulate on the amount of power to be

procured resulting in enhanced value.

The Utility industry is facing an inflection point where technology shifts from Machine 2 Machine (M2M) & Big Data Analytics are profoundly disrupting business models. Flutura strongly believes that Utilities are witnessing enormous change and must respond to changes enabled. M2M and Big data analytics offer fantastic opportunities for monetizing from investments in Smart meter infrastructure.

Read more about how we do it:

Flutura M2M in Oil and Gas



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