April 24, 2014

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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