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Energy’s Digital Revolution
Emerging Platform Leaders
Peter Evans, PhD
Vice President
Center for Global Enterprise July 25, 2014
MIT Platform
Strategy Summit
Photo by Maria Carrasco Rodriguez
Forces reshaping energy markets
Intelligence about energy is dramatically expanding
Source: John Canny, “Designing with Data”, UC Berkeley, EECS, July 2013
New dynamics
1. Volume and velocity of data growing at
- machine level
- facility level
- fleet level
- network level
2. Expanded monitoring/automation
3. Shift from the reactive to the predictive
4. Rise of matching platforms
5. Experimentation with app stores
4
Perspective of scale… US quick energy facts
6
$364 billion
350,000
Housing units
Commercial buildings
Large industrial facilities
125,000,000
5,000,000
US spend on electricity
Source: Revenue from Retail Sales of Electricity to Ultimate Customers by End-Use Sector,
EIA, Electric Power Annual, December 2013. http://www.eia.gov/electricity/annual/
US thermal power plant fleet
7
12million man-hours to serviceannually*
Thermal
plant
Source: UDI World Electric Power
Plants Database, Platts, 2012
Buildings = major source of energy demand
Residential and
commercial buildings
make up nearly 40% of
U.S. energy consumption
6
Data sources
6
Utilities
Industrial facilities
Commercial
Residential
Emerging platforms tap different sources information
Meters
Other sensors
Thermostats
People
Supply-side platforms
“Solar designers” use satellite
imagery and a sophisticated
set of algorithms to remotely
design solar panel systems.
Speed
Lower cost
Greater reach
Source: Craig Rubens, “Sungevity: Where Solar Rooftops Meet the Internet” Gigaom, April 21, 2008.
From offices in the Bay Area can
size systems in Indiana or India
Reduce inefficient truck rolls
Customer quote within 24 hours
Benefits
Internet+ digital imaging = fewer truck rolls
8
Demand-side platforms
8
Johnson Controls’ cloud-based apps store
• Review the performance of one
piece of equipment, an entire
building, or compare hundreds of
facilities around the world.
• Pinpoint equipment that’s
wasting energy
• Monitor and report on carbon
emissions and energy efficiency
App enabled commercial and residential building control systems
Micro approaches to building intelligence
9
New platform solutions are emerging that can efficiently gather the
sensor data from humans, improving information flows between building
occupants and facility managers, boosting comfort and productivity.
Facility managers receive
aggregated “comfort reports”
Building occupants
report conditions
Source: CrowdComfort, MIT Platform Strategy Summit, July 2014
Tapping “human-based” sensor technology
Big data analytic approaches
10
1
Determine key building parameters
and begin load disaggregation.
DETECT ATTRIBUTES
Generate unique models of how the
buildings is, and could be, performing.
2 CREATE ENERGY MODELS
Compare building to efficient model.
3 COMPARE PERFORMANCE
Target best prospects, automate audits and track efficiency savings
Data Sources: Meter + Weather + Building info
Source: Retroficiency, MIT Platform Strategy Summit, July 2014
Analytic steps
Wave of “energy intelligence” startups
10
Source: CGE platform
database, 2014
Twenty-two new companies launched since 2003
Noesis
Strategic questions
12
• Where are the greatest opportunities for platform
companies to grow in the energy sector?
• Are there strong network effects around energy supply
or demand that new platform companies can exploit?
• Are there regulatory impediments that slow the growth
of platform plays?
Energy’s Digital Revolution
Emerging Platform Leaders
Peter Evans, PhD
Vice President
Center for Global Enterprise July 25, 2014
MIT Platform
Strategy Summit

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Energy’s Digital Revolution: Emerging Platform Leaders

  • 1. Energy’s Digital Revolution Emerging Platform Leaders Peter Evans, PhD Vice President Center for Global Enterprise July 25, 2014 MIT Platform Strategy Summit Photo by Maria Carrasco Rodriguez
  • 2. Forces reshaping energy markets Intelligence about energy is dramatically expanding Source: John Canny, “Designing with Data”, UC Berkeley, EECS, July 2013 New dynamics 1. Volume and velocity of data growing at - machine level - facility level - fleet level - network level 2. Expanded monitoring/automation 3. Shift from the reactive to the predictive 4. Rise of matching platforms 5. Experimentation with app stores 4
  • 3. Perspective of scale… US quick energy facts 6 $364 billion 350,000 Housing units Commercial buildings Large industrial facilities 125,000,000 5,000,000 US spend on electricity Source: Revenue from Retail Sales of Electricity to Ultimate Customers by End-Use Sector, EIA, Electric Power Annual, December 2013. http://www.eia.gov/electricity/annual/
  • 4. US thermal power plant fleet 7 12million man-hours to serviceannually* Thermal plant Source: UDI World Electric Power Plants Database, Platts, 2012
  • 5. Buildings = major source of energy demand Residential and commercial buildings make up nearly 40% of U.S. energy consumption 6
  • 6. Data sources 6 Utilities Industrial facilities Commercial Residential Emerging platforms tap different sources information Meters Other sensors Thermostats People
  • 7. Supply-side platforms “Solar designers” use satellite imagery and a sophisticated set of algorithms to remotely design solar panel systems. Speed Lower cost Greater reach Source: Craig Rubens, “Sungevity: Where Solar Rooftops Meet the Internet” Gigaom, April 21, 2008. From offices in the Bay Area can size systems in Indiana or India Reduce inefficient truck rolls Customer quote within 24 hours Benefits Internet+ digital imaging = fewer truck rolls 8
  • 8. Demand-side platforms 8 Johnson Controls’ cloud-based apps store • Review the performance of one piece of equipment, an entire building, or compare hundreds of facilities around the world. • Pinpoint equipment that’s wasting energy • Monitor and report on carbon emissions and energy efficiency App enabled commercial and residential building control systems
  • 9. Micro approaches to building intelligence 9 New platform solutions are emerging that can efficiently gather the sensor data from humans, improving information flows between building occupants and facility managers, boosting comfort and productivity. Facility managers receive aggregated “comfort reports” Building occupants report conditions Source: CrowdComfort, MIT Platform Strategy Summit, July 2014 Tapping “human-based” sensor technology
  • 10. Big data analytic approaches 10 1 Determine key building parameters and begin load disaggregation. DETECT ATTRIBUTES Generate unique models of how the buildings is, and could be, performing. 2 CREATE ENERGY MODELS Compare building to efficient model. 3 COMPARE PERFORMANCE Target best prospects, automate audits and track efficiency savings Data Sources: Meter + Weather + Building info Source: Retroficiency, MIT Platform Strategy Summit, July 2014 Analytic steps
  • 11. Wave of “energy intelligence” startups 10 Source: CGE platform database, 2014 Twenty-two new companies launched since 2003 Noesis
  • 12. Strategic questions 12 • Where are the greatest opportunities for platform companies to grow in the energy sector? • Are there strong network effects around energy supply or demand that new platform companies can exploit? • Are there regulatory impediments that slow the growth of platform plays?
  • 13. Energy’s Digital Revolution Emerging Platform Leaders Peter Evans, PhD Vice President Center for Global Enterprise July 25, 2014 MIT Platform Strategy Summit