Stéphanie LakkisFranz Winterauer

The power of data analytics to drive more effective wind power forecasting

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The wind power challenge

Source: Wind in power 2014 European Statistics, EWEA

The importance of wind power is growing

Total installed capacity in 2000 in the EU

12,9 GW

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The wind power challenge

Source: Wind in power 2014 European Statistics, EWEA

The importance of wind power is growing

Total installed capacity in 2014128.8 GW

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The wind power challenge

Source: http://energinet.dk/EN/El/Nyheder/Sider/Vindmoeller-slog-rekord-i-2014.aspx

Wind power penetration in Denmark in 2014

In 2014, wind power provided Denmark almost

40% of its electricity

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The wind power challenge

Source: Wind in power 2014 European Statistics, EWEA

What we already achieved in 2014…

EU consumption met by wind10,2%

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2020 Targets:

20% of RESin the EU

The wind power challenge

Source: https://ec.europa.eu/energy/en/topics/renewable-energy

…what we’re supposed to reach

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The wind power challenge

In 2014, wind power provided Denmark almost 40% of its electricity

Better wind power forecasting is the key

128.8 GW Total installed capacity in 2014

12,9 GW Total installed capacity in 2000 in the EU

10,2% EU consumption met by wind

2020 Targets: 20% of RES in the EU

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Who needs a wind power forecast?

Distribution System Operators (DSOs)

Generation companies and Independent Power

Producers (IPPs)Transmission System

Operators (TSOs)

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Wind power management today

Forecast algorithm

Online measurements of wind power and meteorological data

Numerical Weather Prediction (NWP)

δ Prediction of future MW production

Congestion management Reserve monitoring Expected energy

balance Market trading

Applications that are or can be impacted by wind power forecasting

δ

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….with additional wind power forecasts… and additional data

Online measurements of wind power and meteorological data

Numerical Weather Prediction (NWP)

δ1Prediction of future MW production

Congestion management Reserve monitoring Expected energy

balance Market trading

Forecast algorithm

Applications that are or can be impacted by wind power forecasting

δ

Additional weather data

Smart combination of different wind power forecasts

δ2 δ3 δn

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How can utilities make the best out of their existing solutions for

a more accurate final forecast and better insight into related

data?

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Smart combination of different forecasts helps to harvest their real “hidden” potential

First finding: Different data sequences leading to best FC from A, B or C

Primary wind direction

Secondary wind direction? ? ?

Q: How to get a Smart Combination?A: By looking into the details!

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Finding the needle in the haystack

Q: Is Forecast “A” always good?A: Certainly not! Good: 45%

Bad: 25%

Avg: 30%

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Finding the needle in the haystack

Q: When is FC “A” not good?A. Forecast “B” and “C” are better with negative fast ramps! FC “C”

FC “A”

FC “B”

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Finding the needle in the haystack

FC “A” is in most cases the best (as shown earlier),but slope is much better at FC “B” from 0-30hrs)

Smart combination of best value (from FC “A”) and best Slope(from FC “B”) immediately led to a better day-ahead forecast

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The benefits

+2

5

3

4

1Enables higher accuracy

Better forecasting of unusual situations

Optimize use of existing wind power forecast solution

Better decision making

Reduces complexity through solution rationalization

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Thank you!

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