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Rooftop PV data & AI:
The shift beyond the
buzz
Dutch Power event
AI - Navigeren naar de toekomst?
Veenendaal, 2024-05-28
Speakers
Daniel has a background
in data science and
machine learning. Daniel
has been working as a
Data Scientist at Eliq since
2021, creating software
that empowers clients
with energy intelligence.
Joost is an experienced
business development
professional at Eliq, with a
background in energy
automation. Joost has a
deep understanding of the
energy sector complexity.
Daniel Anderton Joost van der Linde
DATA
SCIENTIST
HEAD OF BUSINESS
DEVELOPMENT,
BENELUX
The open energy data platform
3
Offices
10+
Markets
100bn
Datapoints
Agenda
❏ Case intro: PV Disaggregation
❏ Deepdive: How AI works for PV Disaggregation
Residential PV in Europe: A Cradle
Of Hypergrowth
Since 2021, installed PV capacity in Europe grew to 260 GW (+95GW!).
Residential PV = 50% of the total installations in Europe.
2027 Forecast for annual installed rooftop capacity = +126% increase
Accelerated by the market (cost 🠝 / efficiency 🠝 ) and regulations, e.g.:
Energy Performance of Buildings Directive &
EU Solar Energy Strategy / European Solar Rooftops Initiative
Future Homes Standard
Netting
Sources: Global Market Outlook for Solar Power 2023-2027
• 24.4GW (17% increase in
2023, outperforming
forecasts) = 1.4kW per
resident.1
• Residential PV about 1.2kW
(or 3 panels) per home,
x5 the European average!2
• About 2.6m prosumer
homes, a 32% adoption
rate3
World champion
in installed PV
Sources: 1 Nationaal Solar Trendrapport 2024 ©Dutch New Energy Research. // 2 Netbeheer Nederland // 3 Global Market Outlook for Solar Power 2023-2027
Netherlands:
Here & now
● Volatility (Δ volume ⇒ Δ price)
● Cost (Who is paying?)
● Congestion (Can we handle it?)
● Imbalance (Atypical volumes)
Supply oriented
market
Collateral damage
Impact on:
● Consumer/Prosumer
● Supplier
● Grid operator
● System operator
Demand oriented
market
Adapt
Impact on:
● Consumer/Prosumer
● Supplier
● Grid operator
● System operator
This requires
More control
Better forecasting
Better insights
1
2
3
OR?
This requires
More control
Better forecasting
Better insights
1
2
3
Availability Ease-of-use
Smart meter
In-home sensors
PV inverter
✓
𝗫
✓
𝗫
𝗫
✓
Better insights
require data
To support a wide audience
- What is my consumption?
Not on the meter
Bridging the
data gap for
prosumers
- What is my generation?
Not on the meter
- When do I consume? Does it
match my generation?
Not on the meter
- How am I doing compared to
others?
Not on the meter
- Where is my energy coming
from?
Only grid exchange on the meter
Availability Ease-of-use Useability
Smart meter
In-home sensors
PV inverter
𝗫
𝗫
✓ ✓
✓
𝗫 𝗫
𝗫
𝗫
PV Disaggregation
bridges the gap with AI
Calculate
consumption
✓ Understand
consumption
✓ Optimise
self-consumption
✓ Shift consumption
Accurately
determine PV
generation
Agenda
✓ Case intro: PV Disaggregation
❏ How AI works for PV Disaggregation
Artificial
Intelligence
(AI)
Machine
Learning
(ML)
Deep
Learning
What is AI?
• Large datasets available
• Time to train models
ML requirements:
What is ML appropriate for?
• Large volume of data
• Solvable with a single model
• Enough time to train the model
Appropriate for problems with:
Example:
Recommendation models
● Large dataset of
interactions with products
● Model does not have to be
trained for every user
● Model can be trained
offline
What data is available?
Available at “Test” time.
Net Metering Data
Available at “Train” time.
Solar Production Data*
Geohash location data
Location Data
Temperature, humidity,
precipitation, cloud
coverage, etc.
Weather Data
Universal Weather-Solar
Effect
Supervised machine learning on
historical solar data from various
locations to understand weather
impacts on solar generation.
Clear Sky Generation
Model (location specific)
Uses a location’s net meter data to
estimate the maximum potential solar
generation under clear skies, utilising
known relationships between the Sun,
Earth, and solar panel configuration.
Methodology
Based on “SunDance: Black-box Behind-the-Meter Solar Disaggregation". D. Chen and D. Irwin, 2017.
● Weather Data
● Solar production data
● ML Model that predicts
the % reduction in solar
irradiance due to
weather conditions
General Weather Model
● Weather Data
● Solar production data
● ML Model that predicts
the % reduction in solar
irradiance due to
weather conditions
General Weather Model
General Weather Model
Location’s Clear Sky Generation
21
● Weather Data
● Location Data
● Export Meter Data
● Solar panel
characteristics
● Theoretical maximum
solar generation
𝚯 Sun’s zenith angle above
𝛂 Sun’s azimuth
𝜷 Solar module tilt angle
𝜙 Solar module azimuth
k Size and efficiency constant
Location’s Clear Sky Generation
𝜷 * 𝜙 * k
Estimating PV parameters
Location’s Clear Sky Generation
New Location with export
energy data added
PV Parameters
Estimated
New export energy
uploaded
PV Disaggregation
triggered for a location
Disaggregated data
written into databases
Process
Algorithm Tricks
No Filter - on whole
year
Highest 100
days avg export
(year)
Highest 50
days avg export
(year)
Highest 10
days (year) & 3
days (month)
avg export
➔ Less Data: Improving the run time for PV parameter estimation (48
cores machine).
Highest 3
days avg export
(month)
51s
180s
540s 45s
12X Improvement
36s
➔ Better Compute Engine: 50% Improvement using Ray over Loky
Presentatie 8. Joost van der Linde & Daniel Anderton - Eliq 28 mei 2024
Results
Data: 69 locations with PV installations. 1 year of 15 minutes import/export
smart meter data.
Min MdAPE* Max MdAPE Avg. MdAPE
Daily aggregations 6.13% 41.21% 13.46%
Monthly aggregations 2.42% 46.20% 10.76%
Yearly aggregations 0.16% 38.13% 8.67%
*MdAPE – Median absolute percentage error between estimated and observed solar production
*MAPE – Mean absolute percentage error between estimated and observed solar production
*APE – Absolute percentage error between estimated and observed solar production
Talk to us
Joost van der Linde
+31 6 11371819
joost@eliq.com
www.eliq.com

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Presentatie 8. Joost van der Linde & Daniel Anderton - Eliq 28 mei 2024

  • 1. Rooftop PV data & AI: The shift beyond the buzz Dutch Power event AI - Navigeren naar de toekomst? Veenendaal, 2024-05-28
  • 2. Speakers Daniel has a background in data science and machine learning. Daniel has been working as a Data Scientist at Eliq since 2021, creating software that empowers clients with energy intelligence. Joost is an experienced business development professional at Eliq, with a background in energy automation. Joost has a deep understanding of the energy sector complexity. Daniel Anderton Joost van der Linde DATA SCIENTIST HEAD OF BUSINESS DEVELOPMENT, BENELUX
  • 3. The open energy data platform 3 Offices 10+ Markets 100bn Datapoints
  • 4. Agenda ❏ Case intro: PV Disaggregation ❏ Deepdive: How AI works for PV Disaggregation
  • 5. Residential PV in Europe: A Cradle Of Hypergrowth Since 2021, installed PV capacity in Europe grew to 260 GW (+95GW!). Residential PV = 50% of the total installations in Europe. 2027 Forecast for annual installed rooftop capacity = +126% increase Accelerated by the market (cost 🠝 / efficiency 🠝 ) and regulations, e.g.: Energy Performance of Buildings Directive & EU Solar Energy Strategy / European Solar Rooftops Initiative Future Homes Standard Netting Sources: Global Market Outlook for Solar Power 2023-2027
  • 6. • 24.4GW (17% increase in 2023, outperforming forecasts) = 1.4kW per resident.1 • Residential PV about 1.2kW (or 3 panels) per home, x5 the European average!2 • About 2.6m prosumer homes, a 32% adoption rate3 World champion in installed PV Sources: 1 Nationaal Solar Trendrapport 2024 ©Dutch New Energy Research. // 2 Netbeheer Nederland // 3 Global Market Outlook for Solar Power 2023-2027 Netherlands:
  • 7. Here & now ● Volatility (Δ volume ⇒ Δ price) ● Cost (Who is paying?) ● Congestion (Can we handle it?) ● Imbalance (Atypical volumes) Supply oriented market Collateral damage Impact on: ● Consumer/Prosumer ● Supplier ● Grid operator ● System operator Demand oriented market Adapt Impact on: ● Consumer/Prosumer ● Supplier ● Grid operator ● System operator
  • 8. This requires More control Better forecasting Better insights 1 2 3 OR?
  • 9. This requires More control Better forecasting Better insights 1 2 3
  • 10. Availability Ease-of-use Smart meter In-home sensors PV inverter ✓ 𝗫 ✓ 𝗫 𝗫 ✓ Better insights require data To support a wide audience
  • 11. - What is my consumption? Not on the meter Bridging the data gap for prosumers - What is my generation? Not on the meter - When do I consume? Does it match my generation? Not on the meter - How am I doing compared to others? Not on the meter - Where is my energy coming from? Only grid exchange on the meter Availability Ease-of-use Useability Smart meter In-home sensors PV inverter 𝗫 𝗫 ✓ ✓ ✓ 𝗫 𝗫 𝗫 𝗫
  • 12. PV Disaggregation bridges the gap with AI Calculate consumption ✓ Understand consumption ✓ Optimise self-consumption ✓ Shift consumption Accurately determine PV generation
  • 13. Agenda ✓ Case intro: PV Disaggregation ❏ How AI works for PV Disaggregation
  • 15. • Large datasets available • Time to train models ML requirements: What is ML appropriate for? • Large volume of data • Solvable with a single model • Enough time to train the model Appropriate for problems with: Example: Recommendation models ● Large dataset of interactions with products ● Model does not have to be trained for every user ● Model can be trained offline
  • 16. What data is available? Available at “Test” time. Net Metering Data Available at “Train” time. Solar Production Data* Geohash location data Location Data Temperature, humidity, precipitation, cloud coverage, etc. Weather Data
  • 17. Universal Weather-Solar Effect Supervised machine learning on historical solar data from various locations to understand weather impacts on solar generation. Clear Sky Generation Model (location specific) Uses a location’s net meter data to estimate the maximum potential solar generation under clear skies, utilising known relationships between the Sun, Earth, and solar panel configuration. Methodology Based on “SunDance: Black-box Behind-the-Meter Solar Disaggregation". D. Chen and D. Irwin, 2017.
  • 18. ● Weather Data ● Solar production data ● ML Model that predicts the % reduction in solar irradiance due to weather conditions General Weather Model
  • 19. ● Weather Data ● Solar production data ● ML Model that predicts the % reduction in solar irradiance due to weather conditions General Weather Model
  • 21. Location’s Clear Sky Generation 21 ● Weather Data ● Location Data ● Export Meter Data ● Solar panel characteristics ● Theoretical maximum solar generation
  • 22. 𝚯 Sun’s zenith angle above 𝛂 Sun’s azimuth 𝜷 Solar module tilt angle 𝜙 Solar module azimuth k Size and efficiency constant Location’s Clear Sky Generation
  • 23. 𝜷 * 𝜙 * k Estimating PV parameters Location’s Clear Sky Generation
  • 24. New Location with export energy data added PV Parameters Estimated New export energy uploaded PV Disaggregation triggered for a location Disaggregated data written into databases Process
  • 25. Algorithm Tricks No Filter - on whole year Highest 100 days avg export (year) Highest 50 days avg export (year) Highest 10 days (year) & 3 days (month) avg export ➔ Less Data: Improving the run time for PV parameter estimation (48 cores machine). Highest 3 days avg export (month) 51s 180s 540s 45s 12X Improvement 36s ➔ Better Compute Engine: 50% Improvement using Ray over Loky
  • 27. Results Data: 69 locations with PV installations. 1 year of 15 minutes import/export smart meter data. Min MdAPE* Max MdAPE Avg. MdAPE Daily aggregations 6.13% 41.21% 13.46% Monthly aggregations 2.42% 46.20% 10.76% Yearly aggregations 0.16% 38.13% 8.67% *MdAPE – Median absolute percentage error between estimated and observed solar production *MAPE – Mean absolute percentage error between estimated and observed solar production *APE – Absolute percentage error between estimated and observed solar production
  • 28. Talk to us Joost van der Linde +31 6 11371819 joost@eliq.com www.eliq.com