The document discusses the transformative potential of big data analytics and machine-to-machine (M2M) technology in the utility sector, particularly through the use of smart meter data. It presents a 7-point framework aimed at unlocking value from this data, emphasizing the importance of consumer energy habit analysis, dynamic pricing, and predictive models for outage prevention. Flutura, the company behind the analysis, highlights the need for utilities to adapt swiftly to capitalize on these advancements and enhance operational efficiency.

1. WAYS TO UNLOCK VALUE FROM
SMART METER BIG DATA7Jobil Louis, Allen & Swapnil
Flutura Decision Sciences & Analytics

2. Technology shifts occur in history periodically and change the rules of the game. It is Flutura's belief that
Machine 2 Machine (M2M) & Big Data Analytics are two such phenomenons which are profoundly disrupting
business models globally. M2M + Big Data Analytics offer revolutionary opportunities by harvesting behavioural
patterns which were previously not visible and provide breakthrough answers to powerful questions.
The Utility Sector for example, is ripe for unlocking energy efficiencies. This can be done by understanding the
energy consumption habit patterns at a level of granularity which was previously not possible – neighbourhood
& consumer level. Furthermore, it also enables you to reduce technical and commercial losses along the
complete grid value chain. Flutura would like to present an outline of a 7 point framework to unlock value
embedded within ‘Smart Grid’ data.
Outage Predictor
Distribution
Transformer
Interventions
Consumer Energy
Habit Gamification
TOU (Time of Use)
Dynamic Pricing Models
Guzzler
Micro Segmentation
Extract Device
Signatures
Bottom up Energy
Demand Forecast
1
2
3
4
5
6
7

3. The last three years have seen a paradigm shift in the increase of data points (by using more instruments)
resulting in a sudden data avalanche for the Utility sector. This has been driven by two waves and Utility
companies need to make sense of it. In the first wave, as Smart meters proliferate, Utilities have to process data
at 15 minute intervals which is a 3000 fold increase in daily data processing. In the second wave, as the number
of SCADA devices which are metering energy flow throughout the grid (like substations, transformers and other
elements of the distribution systems) increases there will be a next level of data explosion. The massive release
of data from Utility grids has profound implications for the industry as it opens up a huge set of possibilities to
monetize these massive grid data pools.
ROI on money already invested
xxx dollars are spent on smart grid investments. One of the challenges is to demonstrate the value unlocked from
smart meter data.
Questions Utility Companies are asking
• How can we monetize the massive smart grid data which has been collected
from millions of customers spanning billions of events at intra hour level?
• How can we see energy consumption patterns not seen before and drive last mile changes?
Smart grid analytics is a graceful blend of art and science and success is possible if one
harmonizes these two dimensions.
How can one unlock value from massive Smart meter investments using analytics?
1
Energy economics from Guzzler segmentation
DATA
1 Energy Habits data
2 Consumer profiles
3 Location data
TARGETED ACTIONS
3
1 Targeted Energy Audit
2 RT SMS behaviour alerts
3 Neighborhood Gamification
4 Bill“what if” energy calculators
4
2 GUZZLER MICRO SEGMENTATION CEREBRA APP
MONETIZATION – IMPACT QUANTIFICATION

4. Context:
Peak power demand is a frequent source
of concern for utility. Before smart meters,
the meter reading frequency was once a month.
As a result one could not specifically pinpoint
consumers who were responsible for ‘guzzling’
power. Now with Smart grid data one can have
granular energy consumption patterns in an
hourly on 15 min interval time frame. Now, it’s
even possible to micro segment consumers based
on the amount of power they consume, their
deviation from baseline consumption, consumer
type and location.
Unanswered questions
• Are the numbers of ‘guzzlers’ increasing or decreasing with time?
• Which segment type experienced maximum surge in ‘guzzler’ migration?
• What is the change in habit between peak time ‘guzzlers’ and night time ‘guzzlers’?
Actions triggered
Energy audits can be offered to ‘guzzlers’ to encourage them to optimize their equipment. For example, in the
industrial segment, factories may have a large number of inefficient motor or pumps which have been habitually
‘guzzling’ a lot of power.
Smart meter value-1
Pinpoint grassroots level neighborhood guzzlers
Blind spots during peak power when grid gets taxed

5. Price is an untapped lever in utility
Today, two large sectors like the Airlines and Retail have dynamic pricing. Why not consider the same in the
Utility sector? What’s more, in order to sensitize people towards energy consumption, it imay be relevant
to have ‘time-of-use’ pricing. For example, peak power tariff for industrial units would be different from peak
power tariff for hospitals and government entities. Another opportunity is to increase the tariff for individual
households who have two-sigma-variance compared to neighborhood baselines.
• Which households are responding to peak power price?
• Do we need to have more pricing slabs?
• How much should we recalibrate pricing to optimize energy usage?
• What is the tipping price at which consumers become sensitive to energy usage?
Smart meter value-2
Time of use pricing

6. Smart meter value-3
‘Gamification’ of consumer energy habits
‘Gamification’ is about integrating gaming frameworks to alter
energy habits of specific high value guzzlers and engaging them.
For example, if in a neighborhood there are 2000 individual
households and in the bill if one puts a big bold statement
saying, "You are in the top 100 energy consumers in your
neighborhood" or "Congrats you have altered your habits
to climb-down from the top 100 list" or "Your change in energy
habits has earned you 100 points which you can redeem at the
local store". Consumers are creatures of habit and if their
change of habit is benchmarked in the neighborhood and
rewarded, their sensitivity towards peak power usage is highly
likely to change. In order to put this into action Utility
companies have to delineate target energy behaviors – say
households not consuming above a certain average threshold
during peak power and create activity loops when that pattern
is detected.
Human beings are
creatures of habit

7. Smart meter value-4
Signature extraction & Habit design
There are individual devices within a commercial organisation or household which typically consume more
energy than others – for example heaters, dish washers, etc. These energy intensive appliances can be put
on a watch list and their consumption signatures detected. This consists of analyzing changes in the voltage
and current going into a house from the smart meter time series data and inferring the specific individual
energy consumption of appliances. The appliance signatures can be decoded by identifying patterns in the
variation in measured power change each time an appliance is switched ON or OFF. Once these appliance
signatures are detected, utility companies can provide tailored energy feedback in their bills to influence
their habits.
• What are the devices in the appliance watch list?
• What are the energy consumption signatures of these appliances in the watch list?
• How do we direct the consumer’s attention to appliances or actions that have high energy saving potential?

8. Smart meter value-5
Predictive models for preventative outage hotspots
Smart meter value-6
Next best Distribution transformers interventions
The harmonic distortion of current is increasing with the enhanced use of nonlinear loads from solid state
devices. Examples of nonlinear loads are personal computer, laptop, laser printer, fax machine, television set
(TV), fluorescent tube with electronic ballast, compact fluorescent lamp, battery charger, adjustable speed
drives, uninterrupted power supply (UPS) and any other equipment powered by switched-mode power supply
(SMPS) unit.
Strategic customers like hospitals, military establishments and
political establishments are affected to a greater extent when an
outage event happens. Now with machine learning algorithms one
can decode patterns leading up to an outage event – brownout
frequencies, transient voltage, step change in energyconsumption.
Earlier when an outage occurred one could not learn from the
patterns leading to it. For example:
CUSTOMER DATA
DTR DATA
OUTAGE EVENTS
METERING DATA
GRID NETWORK
STRUCTURE
TAMPER EVENTS
8 0 6 5 4 7 3
E L E C T R I C M E T E R
SMART METER INFORMATION LANDSCAPE

9. Smart meter value-7
Reduce spot buying through bottom up forecasting
Load forecasting is currently a top down process which looks at
historical patterns to predict future demand. It's a complex problem
to be solved since energy is a "perishable" item. As a result over
forecasted excess power procured cannot be "stored". At the same
time under forecasting would result in last minute procurement of
power which is extremely expensive. With the availability of granular
data, neighborhood 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.
These nonlinear loads draw more current than the fundamental current and cause overloading of the Distribution
Transformers (DTR). This leads to higher losses, reduces the strength of insulation and subsequently leads
to reduction of useful life of the transformer. Aging of transformer increases due to overheating caused by
overloading. Current harmonics from Smart meter data can be used to identify aging of transformer caused by
harmonics due to non linear loads. It's also compounded by the fact that many of the distribution transformers
in the grid responsible for the last mile distribution of power have not been changed for many years. One can
look at power harmonics data, brownouts, blackouts and transient event data to rank order and prioritize DTRs
in specific neighborhoods where it needs to be replaced. In order to do this DTR master and event data must be
collected and stored in a central smart grid event repository DTR 360:
• Which are the neighborhoods where the last mile DTR performance needs to be analyzed?
• Which segment of customers – industrial, household, strategic needs to be prioritized?
• Which events must be brought into Central Smart Grid Event Repository (CSGER)?
• What is the DTR scoring process we must deploy?
Nonlinear Loads Impact Aging Distribution Transformers

10. So can Utilities get started?
Build Foundational Smart Grid Data Model
Bottom up
demand response
Strategic Outage Hotspots
Outage Frequent
Sequence Analyzer
Time of Use (TOU)
Dynamic Pricing
Distrb Transformer DTR 360
Device Signatures
Outage Events
Customer Data
DTR Data
Tamper Events
Grid Network Structure
Metering Data
100+ Energy Vectors
Signal Detectors
12 Core Energy Markers
Scoring Models
VEEDA
Apriori
Advanced Visualisation
Machine Learning Algorithms
Tame Big data ref arch
Guzzler Micro
Segmentation
Cerebra Smart Meter Nano Apps
Cerebra Signal Studio
Conclusion
The Utility industry is facing an inflection point where technology is shifting to Machine 2 Machine (M2M) & Big
Data Analytics and profoundly disrupting business models. Utilities must act and move quickly to respond to
changes and leverage the advantages. M2M and Big data analytics offer immense opportunities for monetizing
from investments in Smart meter infrastructure.

11. About Flutura
Flutura is a niche big data analytics solutions company with a vision to help contain massive risk exposures for
organizations and radically unlock operational efficiencies. It does this by extracting meaningful signals from data
using Big Data Analytics. The name Flutura stands for butterfly; inspired by nature's greatest transformation from
a caterpillar to a butterfly. We are obsessed with Trust and Transformation and align our daily lives to these core
principles. Flutura is funded by Silicon Valley’s leading venture capital firm The Hive which primarily invests in big
data companies worldwide. Flutura at a very early stage has been identified as among the Top 10 most promising
big data companies by CIO Review, a leading analyst magazine.