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Big Data
for Real People
 alertme
   creating smart homes

   February 2012
pilgrim.beart@alertme.com
@pilgrimbeart




                            4th	
  Annual	
  Smart	
  Grids	
  &	
  Cleanpower	
  2012	
  Conference	
  	
  
                                                                         14	
  June	
  2012	
  Cambridge	
  
                                                                www.cir-­‐strategy.com/events/	
  
Introducing	
  AlertMe	
  
•  PlaKorm	
  for	
  the	
  Smart	
  Home	
  /	
  Connected	
  Home	
  
   –  In-­‐home	
  devices	
  +	
  
   –  Gateway	
  +	
  
   –  Cloud	
  services	
  +	
  	
  
   –  A	
  variety	
  of	
  UI’s	
  
•  Complete	
  with	
  out-­‐of-­‐the-­‐box	
  applicaQons:	
  
   –  Smart	
  Monitoring	
  
   –  Smart	
  Energy	
  
   –  Smart	
  HeaQng	
  
   –  Smart	
  Data	
  
                                                          TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
The	
  Value	
  of	
  Data	
  
•  General	
  market	
  trend	
  from	
  products	
  to	
  services	
  
       –  Data	
  is	
  the	
  lifeblood	
  of	
  any	
  service	
  
•  “Old	
  economy”	
  companies	
  struggle	
  with:	
  
       –  A	
  patchwork	
  of	
  legacy	
  IT	
  systems	
  &	
  processes	
  
       –  e.g.	
  UQlity	
  companies	
  may	
  read	
  meter	
  only	
  once/year	
  
•  “New	
  economy”	
  easily	
  deals	
  with	
  Big	
  Data	
  
       –  Millions	
  of	
  customers,	
  millions	
  of	
  hits/day	
  
	
  

                                                                           TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
DisQlling	
  data	
  
•  AlertMe	
  “reads	
  the	
  meter”	
  once	
  every	
  second	
  
       •  For	
  one	
  home	
  that’s	
  31	
  million	
  readings	
  per	
  year	
  
            –  Data	
  available	
  live	
  both	
  in	
  the	
  home	
  and	
  in	
  the	
  cloud	
  
       •  For	
  whole	
  of	
  UK,	
  that’s	
  1.4	
  Petabytes	
  per	
  year	
  
•  Naïve	
  approach	
  would	
  be:	
  
   –  Store	
  everything,	
  then	
  work	
  out	
  what	
  to	
  do	
  with	
  it	
  
   –  But	
  imagine	
  running	
  a	
  database	
  query	
  on	
  1.4PB	
  of	
  data	
  
•  BeEer	
  approach	
  is:	
  
   –  Pre-­‐compute	
  (like	
  Google)	
  
   –  Compute	
  close	
  to	
  the	
  data,	
  only	
  store	
  what’s	
  necessary	
  
   –  E.g.	
  store	
  Digests	
  
                                                                                                   TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
UK	
  Smart	
  Meters	
  may	
  deserve	
  name	
  
Data	
  available	
  on	
  UK	
  SMHAN	
  
•  ConsumpQon	
  (elec	
  	
  &	
  gas)	
  
      –  Live	
  &	
  cumulaQve	
  
•    HH	
  interval	
  data	
  for	
  13	
  months	
  
•    Tariff	
  	
  
•    Prepay	
  credit	
  
•    Extending	
  to	
  include	
  
      –  Microgen	
  
      –  EV	
  control	
  
      	
  	
  	
  	
  …	
  
                                                         TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
Value	
  
Think	
  of	
  3	
  stages:	
  
           Data	
  →	
  InformaQon	
  →	
  Value	
  
e.g.	
  
     Data	
  =	
  Tariff	
  &	
  consumpQon	
  so	
  far	
  this	
  month	
  
     InformaQon	
  =	
  Predicted	
  bill	
  at	
  end	
  of	
  the	
  month	
  
     Value	
  =	
  Household	
  budgeQng,	
  Avoiding	
  disconnecQon	
  




                                                                  TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
Value	
  from	
  that	
  Smart	
  Meter	
  Data	
  
•    VIS	
  -­‐	
  visibility	
                    •    PRIVACY	
  -­‐	
  change	
  of	
  owner	
  
•    COST	
  -­‐	
  translated	
  into	
  £	
      •    MESSAGE	
  -­‐	
  from	
  uQlity	
  
•    CO2	
  -­‐	
  translated	
  
                                                   •    SMSOK	
  -­‐	
  system	
  working	
  
•    EARN	
  -­‐	
  microgen	
  
•    PRICESIGNAL	
  -­‐	
  ToU,	
  CPP	
           •    TIME	
  -­‐	
  reference	
  
•    COMP	
  -­‐	
  comparaQve	
  norms	
          •    READINGS	
  -­‐	
  to	
  check	
  bill	
  
•    PREDICT	
  -­‐	
  budgeing	
  
•    SWITCH	
  -­‐	
  tariff	
  /	
  supplier	
  
•    PREPAY	
  -­‐	
  and	
  credit/debt	
         See:	
  CEDIG	
  data	
  dic/onary	
  
•    DISCONNECT	
  


                                                                              TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
Value	
  from	
  that	
  SM	
  Data	
  -­‐	
  AnalyQcs	
  
(all	
  enabled	
  by	
  1-­‐second	
  	
  data)	
  

                                                       Examples	
  
     •         ITEMISE	
                               •  “Washing	
  at	
  30°C	
  would	
  save	
  you	
  £34/year”	
  
     •         WARN	
                                  •  “Boiling	
  only	
  a	
  cupful	
  of	
  water	
  in	
  your	
  keEle	
  would	
  
                                                          save	
  you	
  £12/year”	
  
     •         MAINT	
                                 •  “25%	
  of	
  your	
  bill	
  is	
  spent	
  on	
  ‘baseload.’	
  Click	
  for	
  Qps	
  
                                                          on	
  what	
  might	
  be	
  causing	
  this	
  and	
  how	
  to	
  address	
  it”	
  
     •         HEAT	
                                  •  “Your	
  fridge	
  is	
  consuming	
  more	
  than	
  last	
  year	
  –	
  
                                                          perhaps	
  the	
  seals	
  have	
  gone.	
  A	
  new	
  fridge	
  would	
  pay	
  
     •         ASSIST	
                                   for	
  itself	
  in	
  3	
  years”	
  
                                                       •  “You’ve	
  leq	
  the	
  house	
  but	
  your	
  fridge	
  door	
  is	
  open	
  
     •         AUDIT	
                                    (or	
  your	
  iron/over/hair	
  curlers	
  are	
  sQll	
  on)”	
  
                                                       •  “Mum	
  didn’t	
  get	
  up	
  this	
  morning”	
  
     •         AUTOAPP	
                               •  “More	
  of	
  your	
  bill	
  is	
  spent	
  on	
  heaQng	
  than	
  in	
  similar	
  
                                                          homes.”	
  
     •         OPTIM	
                                 •  Ensure	
  EV	
  charged	
  by	
  8am	
  at	
  minimal	
  cost	
  
                                                       •  OpQmising	
  heaQng	
  paEerns	
  around	
  occupancy	
  
                                                                                                                    TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
ADELE	
  Smart	
  Data	
  Engine	
  
            	
  
Alertme	
  DomesQc	
  Energy	
  Load	
  Engine




A	
  model,	
  able	
  to	
  use	
  whatever	
  data	
  is	
  available:	
  
•  Zero	
  data	
  (assume	
  naQonal	
  averages)	
  
•  Basic	
  data	
  (demographics	
  or	
  postcodes)	
  
•  Low-­‐res	
  Energy	
  data	
  	
  (quarterly/monthly	
  reads)	
  
•  Medium-­‐res	
  Energy	
  data	
  (Smart	
  Meter	
  HH)	
  
•  High-­‐res	
  Energy	
  data	
  (live	
  via	
  Consumer	
  Gateway)	
  
•  Temperature	
  and	
  per-­‐appliance,	
  if	
  available	
  
•  Other	
  sources	
  (e.g.	
  customer-­‐volunteered	
  info)	
  
                                                            TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
Value	
  of	
  Big	
  Data	
  to	
  the	
  Consumer	
  
•  InformaQon	
  and	
  Insight	
  (esp.	
  around	
  £££)	
  
    –  e.g.	
  predicted	
  bill,	
  personal	
  energy-­‐saving	
  advice	
  
•  Control	
  
    –  e.g.	
  turn	
  on	
  your	
  heaQng	
  from	
  the	
  airport	
  
•  AutomaQon	
  
    –  e.g.	
  opQmise	
  your	
  heaQng	
  around	
  your	
  life	
  paEerns	
  
•  Simplicity	
  	
  (esp.	
  interoperability	
  &	
  low	
  fricQon)	
  
•  Peace	
  of	
  Mind	
  

                                                                            TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
Value	
  of	
  Big	
  Data	
  to	
  the	
  Channel	
  
•  Higher	
  added-­‐value	
  
    –  Energy	
  Services	
  are	
  higher	
  margin	
  than	
  Energy	
  Retail	
  
•  New	
  Services	
  
    –  Brand	
  presence,	
  loyalty	
  
    –  Bundling	
  
        •  One	
  plaKorm	
  to	
  unify	
  the	
  home	
  
•  Cross-­‐sell,	
  up-­‐sell	
  &	
  e-­‐commerce	
  
•  A	
  game	
  everyone	
  can	
  play:	
  
    –  Might	
  Retailers	
  or	
  Telcos	
  disintermediate	
  UQliQes?	
  
                                                                     TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
Data	
  –	
  the	
  Ground	
  Rules	
  
•  Must	
  be	
  Permissive	
  
   –  It’s	
  the	
  consumer’s	
  data	
  
   –  The	
  consumer	
  chooses	
  to	
  grant	
  access	
  
   –  If	
  it	
  doesn’t	
  benefit	
  them	
  -­‐	
  they	
  won’t!	
  
   –  It’s	
  a	
  trade	
  (like	
  gMail)	
  




                                                                           TwiEer:	
  @alertmesays	
  @pilgrimbeart	
  
Big Data
for Real People
 alertme
   creating smart homes

   February 2012
pilgrim.beart@alertme.com
@pilgrimbeart




                            4th	
  Annual	
  Smart	
  Grids	
  &	
  Cleanpower	
  2012	
  Conference	
  	
  
                                                                         14	
  June	
  2012	
  Cambridge	
  
                                                                www.cir-­‐strategy.com/events/	
  

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Sgcp12 beart-alertme

  • 1. Big Data for Real People alertme creating smart homes February 2012 pilgrim.beart@alertme.com @pilgrimbeart 4th  Annual  Smart  Grids  &  Cleanpower  2012  Conference     14  June  2012  Cambridge   www.cir-­‐strategy.com/events/  
  • 2. Introducing  AlertMe   •  PlaKorm  for  the  Smart  Home  /  Connected  Home   –  In-­‐home  devices  +   –  Gateway  +   –  Cloud  services  +     –  A  variety  of  UI’s   •  Complete  with  out-­‐of-­‐the-­‐box  applicaQons:   –  Smart  Monitoring   –  Smart  Energy   –  Smart  HeaQng   –  Smart  Data   TwiEer:  @alertmesays  @pilgrimbeart  
  • 6. The  Value  of  Data   •  General  market  trend  from  products  to  services   –  Data  is  the  lifeblood  of  any  service   •  “Old  economy”  companies  struggle  with:   –  A  patchwork  of  legacy  IT  systems  &  processes   –  e.g.  UQlity  companies  may  read  meter  only  once/year   •  “New  economy”  easily  deals  with  Big  Data   –  Millions  of  customers,  millions  of  hits/day     TwiEer:  @alertmesays  @pilgrimbeart  
  • 7. DisQlling  data   •  AlertMe  “reads  the  meter”  once  every  second   •  For  one  home  that’s  31  million  readings  per  year   –  Data  available  live  both  in  the  home  and  in  the  cloud   •  For  whole  of  UK,  that’s  1.4  Petabytes  per  year   •  Naïve  approach  would  be:   –  Store  everything,  then  work  out  what  to  do  with  it   –  But  imagine  running  a  database  query  on  1.4PB  of  data   •  BeEer  approach  is:   –  Pre-­‐compute  (like  Google)   –  Compute  close  to  the  data,  only  store  what’s  necessary   –  E.g.  store  Digests   TwiEer:  @alertmesays  @pilgrimbeart  
  • 8. UK  Smart  Meters  may  deserve  name  
  • 9. Data  available  on  UK  SMHAN   •  ConsumpQon  (elec    &  gas)   –  Live  &  cumulaQve   •  HH  interval  data  for  13  months   •  Tariff     •  Prepay  credit   •  Extending  to  include   –  Microgen   –  EV  control          …   TwiEer:  @alertmesays  @pilgrimbeart  
  • 10. Value   Think  of  3  stages:   Data  →  InformaQon  →  Value   e.g.   Data  =  Tariff  &  consumpQon  so  far  this  month   InformaQon  =  Predicted  bill  at  end  of  the  month   Value  =  Household  budgeQng,  Avoiding  disconnecQon   TwiEer:  @alertmesays  @pilgrimbeart  
  • 11. Value  from  that  Smart  Meter  Data   •  VIS  -­‐  visibility   •  PRIVACY  -­‐  change  of  owner   •  COST  -­‐  translated  into  £   •  MESSAGE  -­‐  from  uQlity   •  CO2  -­‐  translated   •  SMSOK  -­‐  system  working   •  EARN  -­‐  microgen   •  PRICESIGNAL  -­‐  ToU,  CPP   •  TIME  -­‐  reference   •  COMP  -­‐  comparaQve  norms   •  READINGS  -­‐  to  check  bill   •  PREDICT  -­‐  budgeing   •  SWITCH  -­‐  tariff  /  supplier   •  PREPAY  -­‐  and  credit/debt   See:  CEDIG  data  dic/onary   •  DISCONNECT   TwiEer:  @alertmesays  @pilgrimbeart  
  • 12. Value  from  that  SM  Data  -­‐  AnalyQcs   (all  enabled  by  1-­‐second    data)   Examples   •  ITEMISE   •  “Washing  at  30°C  would  save  you  £34/year”   •  WARN   •  “Boiling  only  a  cupful  of  water  in  your  keEle  would   save  you  £12/year”   •  MAINT   •  “25%  of  your  bill  is  spent  on  ‘baseload.’  Click  for  Qps   on  what  might  be  causing  this  and  how  to  address  it”   •  HEAT   •  “Your  fridge  is  consuming  more  than  last  year  –   perhaps  the  seals  have  gone.  A  new  fridge  would  pay   •  ASSIST   for  itself  in  3  years”   •  “You’ve  leq  the  house  but  your  fridge  door  is  open   •  AUDIT   (or  your  iron/over/hair  curlers  are  sQll  on)”   •  “Mum  didn’t  get  up  this  morning”   •  AUTOAPP   •  “More  of  your  bill  is  spent  on  heaQng  than  in  similar   homes.”   •  OPTIM   •  Ensure  EV  charged  by  8am  at  minimal  cost   •  OpQmising  heaQng  paEerns  around  occupancy   TwiEer:  @alertmesays  @pilgrimbeart  
  • 13. ADELE  Smart  Data  Engine     Alertme  DomesQc  Energy  Load  Engine A  model,  able  to  use  whatever  data  is  available:   •  Zero  data  (assume  naQonal  averages)   •  Basic  data  (demographics  or  postcodes)   •  Low-­‐res  Energy  data    (quarterly/monthly  reads)   •  Medium-­‐res  Energy  data  (Smart  Meter  HH)   •  High-­‐res  Energy  data  (live  via  Consumer  Gateway)   •  Temperature  and  per-­‐appliance,  if  available   •  Other  sources  (e.g.  customer-­‐volunteered  info)   TwiEer:  @alertmesays  @pilgrimbeart  
  • 14. Value  of  Big  Data  to  the  Consumer   •  InformaQon  and  Insight  (esp.  around  £££)   –  e.g.  predicted  bill,  personal  energy-­‐saving  advice   •  Control   –  e.g.  turn  on  your  heaQng  from  the  airport   •  AutomaQon   –  e.g.  opQmise  your  heaQng  around  your  life  paEerns   •  Simplicity    (esp.  interoperability  &  low  fricQon)   •  Peace  of  Mind   TwiEer:  @alertmesays  @pilgrimbeart  
  • 15. Value  of  Big  Data  to  the  Channel   •  Higher  added-­‐value   –  Energy  Services  are  higher  margin  than  Energy  Retail   •  New  Services   –  Brand  presence,  loyalty   –  Bundling   •  One  plaKorm  to  unify  the  home   •  Cross-­‐sell,  up-­‐sell  &  e-­‐commerce   •  A  game  everyone  can  play:   –  Might  Retailers  or  Telcos  disintermediate  UQliQes?   TwiEer:  @alertmesays  @pilgrimbeart  
  • 16. Data  –  the  Ground  Rules   •  Must  be  Permissive   –  It’s  the  consumer’s  data   –  The  consumer  chooses  to  grant  access   –  If  it  doesn’t  benefit  them  -­‐  they  won’t!   –  It’s  a  trade  (like  gMail)   TwiEer:  @alertmesays  @pilgrimbeart  
  • 17. Big Data for Real People alertme creating smart homes February 2012 pilgrim.beart@alertme.com @pilgrimbeart 4th  Annual  Smart  Grids  &  Cleanpower  2012  Conference     14  June  2012  Cambridge   www.cir-­‐strategy.com/events/