my presentation at icem 2017: from data mining to information extraction: using machine-learning to...
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Climate Change
Matteo De Felice (1), L. Dubus (2), A.
Troccoli (3)
(1) ENEA, Italy
(2) EDF R&D, France
(3) Univ. of East Anglia, UK
From data mining to information
extraction: using machine-
learning to model hydro-power
production in Europe
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Climate
Change
R a t i o n a l e : E C E M ’ s G o a l s
European Climatic Energy Mixes (ECEM): A C3S Sectoral
Information Service for the Energy Sector
ECEM is a 27-month project to develop a proof-of-concept climate
service – or Demonstrator – for the European energy sector. It
will enable the energy industry and policy makers assess how well
different energy supply mixes in Europe will meet demand, over
different time horizons (from seasonal to long-term decadal
planning), focusing on the role climate has in the mixes.
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Climate
Change
• Hydropower is still the dominant RES in many countries: in EU-
28 in 2015 the largest RES accounting for 14.4% of total primary
energy production [source: EUROSTAT]
• It is a flexible source: it’s crucial in high RES penetration
scenarios
w h y ?
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Climate
Change We have three types of HP plants:
1. Run-of-river plants
2. Storage dams
3. Pumped storage
h y d r o p o w e r
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Climate
Change
I n s t a l l e d c a p a c i t y i n E u r o p e
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Climate
Change
• Modelling river flow from weather is hard: need to model
precipitation, run-off, evaporation, snow melt, etc.
• Water in reservoirs can be used for multiple purposes, e.g.
irrigation.
• There are other constraints, such as maintaining navigability on
rivers, fish ladders, water levels for recreation, water cooling for
thermal power plants, etc.
• We need information at basin-level!
C h a l l e n g e s
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Climate
Change • ENTSO-E data on hydro-power generation available on the Transparency Platform.
• Two types: 1) Installed capacity (Installed Generation Capacity Aggregated, 14.1.A) and 2) Hourly generation data (Aggregated Generation per Type, 16.1.B&C).
• Three typologies: pumped storage, run-of-river and poundage, and water reservoir.
• Data available since 1/1/2015 and for this work we have used two years of data (until 31/12/2016).
• Meteorological data (precipitation and snow cover) has been extracted from the ERA-INTERIM considering the period 2014-2016.
D a t a s o u r c e ( s )
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Climate
Change
R e s u l t s
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Climate
Change • Country-level averages
• Daily data
• Precipitation (ERA-INTERIM)
• Snow depth (ERA-INTERIM)
T h e p r e d i c t o r s
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Climate
Change
• Rolling sum of the last N days to predict generation at day t
• What is the best value of N?
• Our approach: find the lag that maximise the correlation
between the rolling sum and the generation
l a g g e d p r e d i c t o r s
Main question: is the connection
between meteorological
predictors and generation
instantaneous?
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Climate
Change
O p t i m i s i n g t h e l a g g e d p r e d i c t o r s
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Climate
Change
O p t i m i s i n g t h e l a g g e d p r e d i c t o r s
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Climate
Change
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Climate
Change
A p p r o a c h
ENTSO-E Transparency Platform Data
ERA-INTERIM Data
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Climate
Change
E x a m p l e s
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Climate
Change
R e s u l t s – F r a n c e , r u n - o f - r i v e r
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Climate
Change
R e s u l t s – F r a n c e , r u n - o f - r i v e r ( n o l a g )
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Climate
Change
R e s u l t s - S p a i n , r e s e r v o i r
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Climate
Change
R e s u l t s - S p a i n , r e s e r v o i r ( n o l a g )
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Climate
Change • Data-driven and generalised modelling of HP generation:
(surprisingly?) good results
• (Another) building block of a simulator of the European power
networks
• Climate-focused analysis on the historical data
• Possibility to use the model to perform predictions?
T a k e a w a y m e s s a g e s
http://ecem.climate.copernicus.eu/