CFD down-scaling and online measurements for short-term wind power forecasting
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The document discusses a hybrid approach for short-term wind power forecasting that combines numerical weather prediction (NWP) and online measurements using an artificial neural network (ANN). By integrating multiple data sources and techniques, the study aims to improve forecasting accuracy, particularly for longer horizons, where the full model outperforms simpler methods. The approach is validated on a large wind farm, demonstrating enhanced performance in predicting wind power output.
![How to mix CFD down-scaling and online measurements
for short-term wind power forecasting:
an artificial neural network application
María Bullido García , Julien Berthaut-Gerentes
1. Micro-Scale Modeling combined with Statistical Learning for Short-Term Wind Power Forecasting, Jeremie JUBAN,
EWEA 2013, Vienna
2. NWP Research and Its Applications for Power Systems in China, Dr Rong Zhu, ICEM 2013, Toulouse
3. Implementation of a Fast Artificial Neural Network Library (FANN), S. Nissen, University of Copenhagen (DIKU),
2003.
Our deterministic approach starts from a Numerical Weather Prediction, which
predicts the “meteorological” wind near the wind farm area. Then, a Computational
Fluid Dynamics tool makes a micro down scaling to provide the future wind
characteristics at the exact location of each wind turbine. Then, thanks to the
power curves of the machines, the air density provided by the NWP, and a
planning maintenance given by the user, the final Power Output is calculated. This
step includes the interaction between the wind turbines, through out the Jensen
wake model.
Usually speaking, Forecast systems are classified along several axes:
• Intraday/Extraday, or Short Term/ Very Short Term. This has to do with the
look ahead time (horizon): from 0 to several hours for Very short term
(Intraday) , from some hours to some days for Short term (Extraday).
• Deterministic/Stochastic. These two opposite concepts use different
scientific tools: physics and mechanics for deterministic approach, statistical
tools and machine learning for stochastic approach.
• Numerical Weather Prediction / Online Data: there are basically two sources
of data in order to perform a power forecasting. The first one is
meteorological predictions, based on global measurements and heavy
numerical calculations, the second is online measurement (for instance from
SCADA system).
For each axis, one concept generally excludes the other. Intraday (Very Short
term) is commonly Stochastic with online measurements while Extraday (Short
term) is usually Deterministic based on NWP data.
This work aims to breakdown these classifications, proposing a unique tool based
on the unification of all these techniques.
Introduction: position of the problem
PO. ID
161
Data sets
Deterministic Forecast: physical approach
Benchmark
Stochastic Approach: Machine Learning
References
EWEA 2014, Barcelona, Spain: Europe’s Premier Wind Energy Event
Presenters
Our Machine Learning procedure is based on an Artificial Neural Network. The
architecture is a supervised feed-forward fully connected network. It is trained
using a backpropagation learning process, and selected thanks to a genetic
algorithm.
The optimization of input variables brought the following final set:
• Raw Power Forecast: the power provided by the deterministic approach
• Scada Production: the instant output power measured in the real wind farm
• Scada Horizon: the time lag between the last measurement and the
forecasted time
• Hour: the forecast time within the day (in order to take into account the
night/day effects, maybe omitted in the deterministic approach)
GFS model Wind generator
characteristics
Global
wind
Local
winds
Raw Power
Forecast
A usual question raised in Machine Learning concerns the historical data
necessary to train the network. In this case, the historical NWP meteorological
data has to be collected, as well as long term measurements on site
(Observations). An automatic batch for re-running the deterministic approach
provides the Raw Power Forecast historical data.
- ANN Power Forecast
Raw Power Forecast
–
Scada Production –
Scada Horizon –
Hour (cyclic) –
2 hidden Layers
Observation date
NWPHorizon
Observation date
NWPHorizon
Observation date
NWPHorizon
Observation date
NWPHorizon
Observation date
NWPHorizon
Observation date
NWPHorizon
Observation date
NWPHorizon
Observation date
NWPHorizon
The NWP runs are provided twice a day and they
cover several days. This provides several forecasts
for each time step, with distinct NWP-horizons. In
order to match with observations (measured data), a
two dimensional vision of time has to be adopted:
Observation time vs NWP-horizon.
Observation date
NWPHorizon
With the addition of online measurements,
with their own horizons, this two dimensional
vision of time has to be changed in a 3D time
vision: Observation time vs NWP-horizon vs
Scada-horizon. A single observation can be
predicted with different combinations of NWP
and Scada horizons.
This process replicates several times the same data (a single measurement is
duplicated along NWP and Scada horizon axes), which makes a strong correlation
between different data points. Thus, constructing the learning/test/validation by
pure randomization results in data snooping (data within different sets are strongly
correlated). A way to avoid this failure is to regroup the data by day before splitting
them into different sets randomly.
We compare the results of the whole process, with those form simpler approaches:
• pure persistence (forecasted power = measured power)
• persistence + ANN Learning,
• pure NWP/CFD (with no calibration at all)
• NWP/CFD + ANN.
Daniel Dias De Oliveira
Sales department manager
María Bullido García
Forecasting Expert
Céline Bezault
Wind Power Expert
This approach is tested on a large wind
farm (99 MW, 66 WTG) on a complex
terrain. We have about 1 year historical
data. There are 4 NWP runs a day, with a
15 min time step, leading to 2,5 millions
of data points (250k=37 days for
validation, 750k=108 days in test set,
1M5= 216 days in the learning set). The
results are based on the Normalized Root
Mean Square Error, binned by Scada
horizons.
0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00
0
5 %
10%
15%
20%
25%
Scada Horizon [H]
RMSEnormalizedbyParkNominalPower
Full approach
Raw persistence
Persistence + ANN
Raw NWP/CFD
NWP/CFD + ANN
For horizon less than 30min, Raw persistence is still preferred. For all longer
horizon, the full model has a smaller error than other solutions. Numerical solutions
(NWP/CFD) beats online measurements after 3~4 hours.](https://image.slidesharecdn.com/howtomixcfddown-scalingandonlinemeasurementsforshort-termwindpowerforecastinganartificialneuralnetwo-140418044535-phpapp01/85/CFD-down-scaling-and-online-measurements-for-short-term-wind-power-forecasting-1-320.jpg)
CFD down-scaling and online measurements for short-term wind power forecasting
- 1. How to mix CFD down-scaling and online measurements for short-term wind power forecasting: an artificial neural network application María Bullido García , Julien Berthaut-Gerentes 1. Micro-Scale Modeling combined with Statistical Learning for Short-Term Wind Power Forecasting, Jeremie JUBAN, EWEA 2013, Vienna 2. NWP Research and Its Applications for Power Systems in China, Dr Rong Zhu, ICEM 2013, Toulouse 3. Implementation of a Fast Artificial Neural Network Library (FANN), S. Nissen, University of Copenhagen (DIKU), 2003. Our deterministic approach starts from a Numerical Weather Prediction, which predicts the “meteorological” wind near the wind farm area. Then, a Computational Fluid Dynamics tool makes a micro down scaling to provide the future wind characteristics at the exact location of each wind turbine. Then, thanks to the power curves of the machines, the air density provided by the NWP, and a planning maintenance given by the user, the final Power Output is calculated. This step includes the interaction between the wind turbines, through out the Jensen wake model. Usually speaking, Forecast systems are classified along several axes: • Intraday/Extraday, or Short Term/ Very Short Term. This has to do with the look ahead time (horizon): from 0 to several hours for Very short term (Intraday) , from some hours to some days for Short term (Extraday). • Deterministic/Stochastic. These two opposite concepts use different scientific tools: physics and mechanics for deterministic approach, statistical tools and machine learning for stochastic approach. • Numerical Weather Prediction / Online Data: there are basically two sources of data in order to perform a power forecasting. The first one is meteorological predictions, based on global measurements and heavy numerical calculations, the second is online measurement (for instance from SCADA system). For each axis, one concept generally excludes the other. Intraday (Very Short term) is commonly Stochastic with online measurements while Extraday (Short term) is usually Deterministic based on NWP data. This work aims to breakdown these classifications, proposing a unique tool based on the unification of all these techniques. Introduction: position of the problem PO. ID 161 Data sets Deterministic Forecast: physical approach Benchmark Stochastic Approach: Machine Learning References EWEA 2014, Barcelona, Spain: Europe’s Premier Wind Energy Event Presenters Our Machine Learning procedure is based on an Artificial Neural Network. The architecture is a supervised feed-forward fully connected network. It is trained using a backpropagation learning process, and selected thanks to a genetic algorithm. The optimization of input variables brought the following final set: • Raw Power Forecast: the power provided by the deterministic approach • Scada Production: the instant output power measured in the real wind farm • Scada Horizon: the time lag between the last measurement and the forecasted time • Hour: the forecast time within the day (in order to take into account the night/day effects, maybe omitted in the deterministic approach) GFS model Wind generator characteristics Global wind Local winds Raw Power Forecast A usual question raised in Machine Learning concerns the historical data necessary to train the network. In this case, the historical NWP meteorological data has to be collected, as well as long term measurements on site (Observations). An automatic batch for re-running the deterministic approach provides the Raw Power Forecast historical data. - ANN Power Forecast Raw Power Forecast – Scada Production – Scada Horizon – Hour (cyclic) – 2 hidden Layers Observation date NWPHorizon Observation date NWPHorizon Observation date NWPHorizon Observation date NWPHorizon Observation date NWPHorizon Observation date NWPHorizon Observation date NWPHorizon Observation date NWPHorizon The NWP runs are provided twice a day and they cover several days. This provides several forecasts for each time step, with distinct NWP-horizons. In order to match with observations (measured data), a two dimensional vision of time has to be adopted: Observation time vs NWP-horizon. Observation date NWPHorizon With the addition of online measurements, with their own horizons, this two dimensional vision of time has to be changed in a 3D time vision: Observation time vs NWP-horizon vs Scada-horizon. A single observation can be predicted with different combinations of NWP and Scada horizons. This process replicates several times the same data (a single measurement is duplicated along NWP and Scada horizon axes), which makes a strong correlation between different data points. Thus, constructing the learning/test/validation by pure randomization results in data snooping (data within different sets are strongly correlated). A way to avoid this failure is to regroup the data by day before splitting them into different sets randomly. We compare the results of the whole process, with those form simpler approaches: • pure persistence (forecasted power = measured power) • persistence + ANN Learning, • pure NWP/CFD (with no calibration at all) • NWP/CFD + ANN. Daniel Dias De Oliveira Sales department manager María Bullido García Forecasting Expert Céline Bezault Wind Power Expert This approach is tested on a large wind farm (99 MW, 66 WTG) on a complex terrain. We have about 1 year historical data. There are 4 NWP runs a day, with a 15 min time step, leading to 2,5 millions of data points (250k=37 days for validation, 750k=108 days in test set, 1M5= 216 days in the learning set). The results are based on the Normalized Root Mean Square Error, binned by Scada horizons. 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 0 5 % 10% 15% 20% 25% Scada Horizon [H] RMSEnormalizedbyParkNominalPower Full approach Raw persistence Persistence + ANN Raw NWP/CFD NWP/CFD + ANN For horizon less than 30min, Raw persistence is still preferred. For all longer horizon, the full model has a smaller error than other solutions. Numerical solutions (NWP/CFD) beats online measurements after 3~4 hours.