State of the Art in Forecasting of Wind and Solar Power...
Transcript of State of the Art in Forecasting of Wind and Solar Power...
State-of-the-art in Forecasting ofWind and Solar Power Generation
Henrik Madsen1, Pierre Pinson, Peder Bacher1,
Jan Kloppenborg1, Julija Tastu1, Emil Banning Iversen1
(1) Department of Applied Mathematics and Computer Science
DTU, DK-2800 Lyngby
www.henrikmadsen.org
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 1
Outline
I shall focus on Wind - and briefly mention Solar and Load:
Status in Denmark
Wind power point forecasting
Use of several providers of MET forecasts
Uncertainty and confidence intervals
Scenario forecasting
Space-time scenario forecasting
Use of stochastic differential equations
Examples on the use of probabilistic forecasts
Optimal bidding for a wind farm owner
Solar power forecasting
Lessons learned in DenmarkWind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 2
Some Wind Power Statistics for Denmark
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 3
Power Grid. A snap-shot from 14. February
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 4
Wind Power Forecasting - History
Our methods for probabilistic wind power forecasting have been implemented in theAnemos Wind Power Prediction System, Australian Wind Energy Forecasting Systems(AWEFS) andWPPT
The methods have been continuously developed since 1993 - incollaboration withEnerginet.dk,Dong Energy,Vattenfall,Risø – DTU Wind,The ANEMOS projects partners/consortium (since 2002),Overspeed GmbH (Anemos: www.overspeed.de/gb/produkte/windpower.html)ENFOR (WPPT: www.enfor.dk)
Used operationally for predicting wind power in Denmark since 1996.
Now used by all major players in Denmark (Energinet.dk, DONG, Vattenfall, ..)
Anemos/WPPT is now used eg in Europe, Australia, and North America.
Often used as forecast engine embedded in other systems.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 5
Prediction Performance
A typical (power curve only!) performance measure. 13 largeparks. Installed power: 1064MW. Period: August 2008 - March 2010. MET input is in all casesECMWF. Criterion used:RMSE (notice:Power Curve model only)
Forecast horizon (hours)
Nor
mal
ized
pow
er
0.00
0.05
0.10
0.15
0 12 24 36 48
Nominal power: 1064 MW
RMSE
WPPT−PCSimple modelCommercial model 1Commercial model 2
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 6
Prediction of wind power
In areas with high penetration of wind power such as the Western part of Denmark and theNorthern part of Germany and Spain, reliable wind power predictions are needed in order toensure safe and economic operation of the power system.
Accurate wind power predictions are needed with different prediction horizons in order toensure
(up to a few hours) efficient and safe use of regulation power (spinning reserve) andthe transmission system,
(12 to 36 hours) efficient trading on the Nordic power exchange, NordPool,
(days) optimal operation of eg. large CHP plants.
Predictions of wind power are needed both for the total supply area as well as on a regionalscale and for single wind farms.
For some grids/in some situations the focus is on methods forramp forecasting, in someother cases the focus in on reliable probabilistic forecasting.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 7
Uncertainty and adaptivity
Errors in MET forecasts will end up in errors in wind power forecasts, but other factors leadto a need for adaptation which however leads to some uncertainties.
The total system consisting of wind farms measured online, wind turbines not measuredonline and meteorological forecasts will inevitably change over time as:
the population of wind turbines changes,
changes in unmodelled or insufficiently modelled characteristics (importantexamples: roughness and dirty blades),
changes in the NWP models.
A wind power prediction system must be able to handle these time-variations in model andsystem. An adequate forecasting system may useadaptive and recursive model estimationto handle these issues.
We started (some 20 years ago) assuming Gaussianity; but this is a very serious (wrong)assumption !
Following the initial installation the software tool will automatically calibrate the models tothe actual situation.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 8
The power curve model
The wind turbine “power curve” model,ptur = f(wtur) is extended to a wind farmmodel, pwf = f(wwf , θwf ), by introducingwind direction dependency. By introducing arepresentative area wind speed and direction itcan be further extended to cover all turbines inan entire region,par = f(w̄ar, θ̄ar).
The power curve model is defined as:
p̂t+k|t = f( w̄t+k|t, θ̄t+k|t, k )
wherew̄t+k|t is forecasted wind speed, andθ̄t+k|t is forecasted wind direction.
The characteristics of the NWP change withthe prediction horizon.
k
Wind speedWind direction
P
k
Wind speedWind direction
P
k
Wind speedWind direction
P
k
Wind speedWind direction
P
HO - Estimated power curve
Plots of the estimated power curve forthe Hollandsbjerg wind farm.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 9
The dynamical prediction model
The power curve models are used as input for an adaptively estimateddynamical model,which (as asimple example) leads to the following k-stop ahead forecasts:
p̂t+k|t = a1pt + a2pt−1 + b p̂pc
t+k|t +
3∑
i=1
[cci cos2iπh24
t+k
24+ c
si sin
2iπh24t+k
24] +m+ et+k
wherept is observed power production,k ∈ [1; 48] (hours) is prediction horizon,̂ppct+k|t is
power curve prediction andh24t+k is time of day.
Model features include
multi-step prediction model to handle non-linearities andunmodelled effects,
the number of terms in the model depends on the prediction horizon,
non-stationarity are handled by adaptive estimation of themodel parameters,
the deviation between observed and forecasted diurnal variation is described usingFourier expansions.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 10
A model for upscaling
The dynamic upscaling model for a region is defined as:
p̂reg
t+k|t = f( w̄art+k|t, θ̄
art+k|t, k ) p̂loct+k|t
wherep̂loct+k|t is a local (dynamic) power prediction within the region,w̄ar
t+k|t is forecasted regional wind speed, and
θ̄art+k|t is forecasted regional wind direction.
The characteristics of the NWP andp̂loc change with the prediction horizon. Hence thedependendency of prediction horizonk in the model.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 11
Configuration Example
This configuration of Anemos PredictionSystem/WPPT is used by a large TSO.Characteristics for the installation:
A large number of wind farms andstand-alone wind turbines.
Frequent changes in the windturbine population.
Offline production data with aresolution of 15 min. is availablefor more than 99% of the windturbines in the area.
Online data for a large numberof wind farms are available. Thenumber of online wind farms in-creases quite frequently.
ModelUpscaling
predictionTotal prod.
Dynamic
Power CurveModel
Model
Offline prod.data
predictionArea prod.
NWP data
Online prod.data
predictionOnline prod.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 12
Fluctuations of offshore wind power
Fluctuations at large offshore wind farms have a significantimpact on the control andmanagement strategies of their power output
Focus is given to the minute scale. Thus, the effects relatedto the turbulent nature ofthe wind are smoothed out
When looking at time-series of power production at Horns Rev(160MW/209MW)and Nysted (165 MW), one observes successive periods with fluctuations of largerand smaller magnitude
We aim at building models
based on historical wind powermeasures only...... but able to reproduce thisobserved behavior
this calls forregime-switchingmodels
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 13
Results - Horns Rev
The evaluation set is
divided in 19 different
periods of different
lengths and
characteristics
MSAR models
generally outperform
the others
In the RADAR@sea
project the regime shift
is linked to convective
rain events – which are
detected by a weather
radar.
2 4 6 8 10 12 14 16 1810
15
20
25
30
Test data set
RM
SE
[kW
]
1 minute Horns Rev SETARMSARSTARARMA
2 4 6 8 10 12 14 16 1820
40
60
80
100
Test data set
RM
SE
[kW
]
5 minute Horns RevSETARMSARSTARARMA
2 4 6 8 10 12 14 16 180
50
100
150
Test data set
RM
SE
[kW
]
10 minute Horns RevSETARMSARSTARARMA
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 14
Spatio-temporal forecasting
Predictive improvement (measured inRMSE) of forecasts errors by adding thespatio-temperal module in WPPT.
23 months (2006-2007)
15 onshore groups
Focus here on 1-hour forecast only
Larger improvements for eastern partof the region
Needed for reliable ramp forecasting.
The EU project NORSEWinD willextend the region
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 15
Combined forecasting
A number of power forecasts areweighted together to form a newimproved power forecast.
These could come from parallelconfigurations of WPPT using NWPinputs fromdifferent METproviders or they could come fromother power prediction providers.
In addition to the improved perfor-mance also the robustness of the sys-tem is increased.
Met Office
DWD
DMI
WPPT
WPPT
WPPT
Comb Final
The example show results achieved for theTunø Knob wind farms using combinationsof up to 3 power forecasts.
Hours since 00Z
RM
S (
MW
)
5 10 15 20
5500
6000
6500
7000
7500
hir02.locmm5.24.loc
C.allC.hir02.loc.AND.mm5.24.loc
Typically an improvement on 10-15 pctin accuracy of the point prediction isseen by including more than one METprovider. Two or more MET providersimply information about uncertainty
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 16
Uncertainty estimation
In many applications it is crucial that a pre-diction tool delivers reliable estimates (prob-abilistc forecasts) of the expected uncertaintyof the wind power prediction.
We consider the following methods for esti-mating the uncertainty of the forecasted windpower production:
Ensemble based - but corrected -quantiles.
Quantile regression.
Stochastic differential equations.
The plots show raw (top) and corrected (bot-tom) uncertainty intervales based on ECMEFensembles for Tunø Knob (offshore park),29/6, 8/10, 10/10 (2003). Shown are the25%, 50%, 75%, quantiles.
Tunø Knob: Nord Pool horizons (init. 29/06/2003 12:00 (GMT), first 12h not in plan)
kW
12:00 18:00 0:00 6:00 12:00 18:00 0:00Jun 30 2003 Jul 1 2003 Jul 2 2003
020
0040
00
Tunø Knob: Nord Pool horizons (init. 08/10/2003 12:00 (GMT), first 12h not in plan)
kW
12:00 18:00 0:00 6:00 12:00 18:00 0:00Oct 9 2003 Oct 10 2003 Oct 11 2003
020
0040
00
Tunø Knob: Nord Pool horizons (init. 10/10/2003 12:00 (GMT), first 12h not in plan)
kW
12:00 18:00 0:00 6:00 12:00 18:00 0:00Oct 11 2003 Oct 12 2003 Oct 13 2003
020
0040
00
Tunø Knob: Nord Pool horizons (init. 29/06/2003 12:00 (GMT), first 12h not in plan)
kW
12:00 18:00 0:00 6:00 12:00 18:00 0:00Jun 30 2003 Jul 1 2003 Jul 2 2003
020
0040
00
Tunø Knob: Nord Pool horizons (init. 08/10/2003 12:00 (GMT), first 12h not in plan)
kW
12:00 18:00 0:00 6:00 12:00 18:00 0:00Oct 9 2003 Oct 10 2003 Oct 11 2003
020
0040
00
Tunø Knob: Nord Pool horizons (init. 10/10/2003 12:00 (GMT), first 12h not in plan)
kW
12:00 18:00 0:00 6:00 12:00 18:00 0:00Oct 11 2003 Oct 12 2003 Oct 13 2003
020
0040
00
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 17
Quantile regression
A (additive) model for each quantile:
Q(τ) = α(τ) + f1(x1; τ) + f2(x2; τ) + . . .+ fp(xp; τ)
Q(τ) Quantile offorecast error from anexisting system.
xj Variables which influence the quantiles, e.g. the wind direction.
α(τ) Intercept to be estimated from data.
fj(·; τ) Functions to be estimated from data.
Notes on quantile regression:
Parameter estimates found by minimizing a dedicated function of the
prediction errors.
The variation of the uncertainty is (partly) explained by the independent
variables.Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 18
Quantile regression - An example
Effect of variables (- the functions are approximated by Spline basis
functions):
0 1000 3000 5000
−20
000
1000
2000
pow.fc
25%
(bl
ue)
and
75%
(re
d) q
uant
iles
20 25 30 35
−20
000
1000
2000
horizon
25%
(bl
ue)
and
75%
(re
d) q
uant
iles
0 50 150 250 350
−20
000
1000
2000
wd10m
25%
(bl
ue)
and
75%
(re
d) q
uant
iles
Forecasted power has a large influence.
The effect of horizon is of less importance.
Some increased uncertainty for Westerly winds.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 19
Example: Probabilistic forecasts
5 10 15 20 25 30 35 40 450
10
20
30
40
50
60
70
80
90
100
look−ahead time [hours]
pow
er [%
of P
n]
90%80%70%60%50%40%30%20%10%pred.meas.
Notice how the confidence intervals varies ...
But the correlation in forecasts errors is not described so far.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 20
Correlation structure of forecast errors
It is important to model theinterdependence structureof the prediction errors.
An example of interdependence covariance matrix:
5 10 15 20 25 30 35 40
5
10
15
20
25
30
35
40
horizon [h]
horiz
on[h
]
−0.2
0
0.2
0.4
0.6
0.8
1
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 21
Correct (top) and naive (bottom) scenarios%
of in
stalle
d ca
pacit
y
0 24 48 72 96 120 144
020
4060
8010
0
hours
100% 90% 80% 70% 60% 50% 40% 30% 20% 10%100% 90% 80% 70% 60% 50% 40% 30% 20% 10%
% o
f insta
lled
capa
city
0 24 48 72 96 120 144
020
4060
8010
0
hours
100% 90% 80% 70% 60% 50% 40% 30% 20% 10%100% 90% 80% 70% 60% 50% 40% 30% 20% 10%
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 22
Use of Stoch. Diff. Equations
The state equation describes the future wind power production
dxt =− θ(ut) · (xt − p̂t|0)dt+
2√
θ(ut)α(ut)p̂t|0(1− p̂t|0)xt · (1− xt)dwt,
with α(ut) ∈ (0, 1), and the observation equation
yh =xth|0 + eh,
whereh ∈ {1, 2, ..., 48}, th = k, eh ∼ N(0, s2), x0 = “observed power at t=0”, and
p̂t|0 point forecast byWPPT (Wind Power Prediction Tool)
ut input vector (heret andp̂t|0)
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 23
Examples of using SDEs
Time
Pow
er
0.0
0.2
0.4
0.6
0.8
1.0
0 12 24 36 48
April 22. 2001, 12h May 19. 2001, 12h
May 5. 2003, 18h
0 12 24 36 48
0.0
0.2
0.4
0.6
0.8
1.0October 22. 2002, 00h
0.0
0.2
0.4
0.6
0.8
1.0
Obs. p̂t|0 p̂SDE
Use of SDEs provides a possibility for a joint description ofboth non-symmetricalconditional densities as well as the interdependence of theforecasts.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 24
SDE approach – Correlation structures
Time
Tim
e
12
24
36
48
12 24 36 48
April 22. 2001, 12h May 19. 2001, 12h
May 5. 2003, 18h
12 24 36 48
12
24
36
48October 22. 2002, 00h
0.0
0.2
0.4
0.6
0.8
1.0
Use of SDEs provides a possibility to model eg. time varying and wind power dependentcorrelation structures.SDEs provide a perfect framework forcombined wind and solar power forecasting.Today bothAnemos Wind Power Prediction SystemandWPPT provide operationsforecasts of both wind and solar power production (used eg. all over Australia)
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 25
Motivation - Space-Time Dependencies
0 10 20 30 40
020
4060
8010
0
horizon [hours]
pow
er [%
Pn]
90% 80% 70% 60% 50% 40% 30% 20% 10% pred.meas
This is not enough...
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 26
Space-time trajectories
0 10 20 30 40
020
4060
8010
0
horizon [hours]
pow
er [%
Pn]
90% 80% 70% 60% 50% 40% 30% 20% 10% pred.meas
Scenario 1Scenario 2Scenario 3Scenario 4Scenario 5
0 10 20 30 40
020
4060
8010
0
horizon [hours]
pow
er [%
Pn]
90% 80% 70% 60% 50% 40% 30% 20% 10% pred.meas
Scenario 1Scenario 2Scenario 3Scenario 4Scenario 5
0 10 20 30 40
020
4060
8010
0
horizon [hours]
pow
er [%
Pn]
90% 80% 70% 60% 50% 40% 30% 20% 10% pred.meas
Scenario 1Scenario 2Scenario 3Scenario 4Scenario 5
0 10 20 30 40
020
4060
8010
0
horizon [hours]
pow
er [%
Pn]
90% 80% 70% 60% 50% 40% 30% 20% 10% pred.meas
Scenario 1Scenario 2Scenario 3Scenario 4Scenario 5
no space-time correlation appropriate space-time correlation
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 28
Type of forecasts required
Point forecasts (normal forecasts); a single value for each time point in the future.Sometimes with simple error bands.
Probabilistic or quantile forecasts; the full conditional distribution for each timepoint in the future.
Scenarios; probabilistic correct scenarios of the future wind power production.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 29
Value of knowing the uncertainties
Case study: A 15 MW wind farm in the Dutch electricity market,prices andmeasurements from the entire year 2002.
From a phd thesis by Pierre Pinson (2006).
The costs are due to the imbalance penalties on the regulation market.
Value of an advanced method for point forecasting:The regulation costs arediminished by nearly 38 pct.compared to the costs of using the persistanceforecasts.
Added value of reliable uncertainties:A further decrease of regulation costs – up to39 pct.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 30
Wind power – asymmetrical penalties
The revenue from trading a specific hour on NordPool can be expressed as
PS × Bid +
{
PD × (Actual− Bid) if Actual > BidPU × (Actual− Bid) if Actual < Bid
PS is the spot price andPD/PU is the down/up reg. price.
The bid maximising the expected revenue is the followingquantile
E[PS ]− E[PD]
E[PU ]− E[PD]
in the conditional distribution of the future wind power production.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 31
Wind power – asymmetrical penalties
It is difficult to know the regulation prices at the day ahead level – research intoforecasting is ongoing.
The expression for the quantile is concerned with expected values of the prices – justgetting these somewhat right will increase the revenue.
A simple tracking ofCD andCU is a starting point.
The bids maximizing the revenue during the period September 2009 to March2010:
Qua
ntile
0.0
0.4
0.8
2009−09−01 2009−11−01 2010−01−01 2010−03−01
Monthly averages Operational tracking
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 32
Sizing of Energy Storage
Correct
Storage (hours of full wind prod.)
Den
sity
−10 −5 0 5 10
0.00
0.10
0.20
0.30
Naive
Storage (hours of full wind prod.)
Den
sity
−10 −5 0 5 10
0.00
0.10
0.20
0.30
(Illustrative example based on 50 day ahead scenarios. Usedfor calculating the risk for a storage to be toosmall)
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 33
Wind and EU cross-border power flows
Map of thenonlinear impact andsensitivity of EU power flowsto predicted wind power penetration in Germany... here if within10-15% of installed capacity
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 34
Solar Power Forecasting
Same principles as for wind power ....
Developed for grid connected PV-systems mainly installed on rooftops
Average of output from 21 PV systems in small village (Brædstrup) in DK
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 35
Method
Based on readings from the systems and weather forecasts
Two-step method
Step One: Transformation to atmospheric transmittanceτ with statistical clear skymodel (see below). Step Two: A dynamic model (see paper).
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 36
Software Modules for Wind Power Forecasting
Point prediction module
Probabilistic (quantile) forecasting module
Scenario generation module
Spatio-temporal forecasting module
Space-time scenario generation module
Even-based prediction module (eg. cut-off prob.)
Ramp prediction module
Same modules are available for solar Power Forecasting
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 38
VG-Integration: Lessons Learned in Denmark
(> 5 pct wind): Tools for Wind/Solar Power forecasting are important
(> 10 pct wind): Tools for reliable probabilistic forecasting are needed
(> 15 pct wind): Consider Energy Systems Integration (not Power alone)
(> 20 pct wind): Consider Methods for Demand Side Management
(> 25 pct wind): New methods for finding the optimal spinning reserve are needed(based on prob. forecasting of wind/solar power production)
Joint forecasts of wind, solar, load and prices are essential
Limited need - or no need - for classical storage solutions
Huge need for virtual storage solutions
Intelligent interaction between power, gas, DH and biomassvery important
ICT and use of data, adaptivity, intelligence, and stochastic modelling is veryimportant
The largest national strategic research project:Centre for IT-Intelligent Energy Systems inCities - CITIES have been launched 1. January 2014. International expertice from NREL(US), UCD/ERC (Ireland), AIT (Austria) becomes important.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 39
DSF Centre for IT-Intelligent Energy Systems
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 40
Wind Power Forecasting - Lessons Learned
The forecasting models must beadaptive (in order to taken changes of dust onblades, changes roughness, etc., into account).
Reliable estimates of theforecast accuracyis very important (check the reliability byeg. reliability diagrams).
Reliable probabilistic forecasts are important to gain thefull economical value.
Usemore than a single MET provider for delivering the input to the prediction tool– this improves the accuracy of wind power forecasts with 10-15 pct.
Estimates of thecorrelation in forecasts errors important.
Forecasts of ’cross dependencies’ between load, prices, wind and solar power areimportant.
Probabilistic forecasts are very important for asymmetric cost functions.
Probabilistic forecasts can provideanswersfor questions likeWhat is the probability that a given storage is large enough for the next 5 hours?What is the probability of an increase in wind power production of more that 50pct of installed power over the next two hours?What is the probability of a down-regulation due to wind power on more than xGW within the next 4 hours.
The same conclusions hold for our tools foreg. solar power forecasting.Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 41
Some Forecasting Tools from DTU
Forecasting and optimisation tools enabling the integration of a large share ofrenewables:
Electricity load forecasts: LoadForWind power production: WPPTSolar power production: SolarForGas load: GasforHeat load: PRESSOptimal operation of CHP systems: PRESSPrice forecasts: PriceForLately: Wave power forecasts
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 42
Some references
H. Madsen:Time Series Analysis, Chapman and Hall, 392 pp, 2008.
J.M. Morales, A.J. Conejo, H. Madsen, P. Pinson, M. Zugno:Integrating Renewables in Electricity
Markes, Springer, 430 pp., 2013.
G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, C. Draxl: The state-of-the-art in
short-term prediction of wind power, ANEMOS plus report, 2011.
P. Meibom, K. Hilger, H. Madsen, D. Vinther:Energy Comes together in Denmark, IEEE Power
and Energy Magazin, Vol. 11, pp. 46-55, 2013.
T.S. Nielsen, A. Joensen, H. Madsen, L. Landberg, G. Giebel:A New Reference for Predicting
Wind Power, Wind Energy, Vol. 1, pp. 29-34, 1999.
H.Aa. Nielsen, H. Madsen:A generalization of some classical time series tools, Computational
Statistics and Data Analysis, Vol. 37, pp. 13-31, 2001.
H. Madsen, P. Pinson, G. Kariniotakis, H.Aa. Nielsen, T.S. Nilsen: Standardizing the performance
evaluation of short-term wind prediction models, Wind Engineering, Vol. 29, pp. 475-489, 2005.
H.A. Nielsen, T.S. Nielsen, H. Madsen, S.I. Pindado, M. Jesus, M. Ignacio:Optimal Combination
of Wind Power Forecasts, Wind Energy, Vol. 10, pp. 471-482, 2007.
A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, F. Feitosa,A review on the young history
of the wind power short-term prediction, Renew. Sustain. Energy Rev., Vol. 12, pp. 1725-1744,
2008.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 43
Some references (Cont.)
J.K. Møller, H. Madsen, H.Aa. Nielsen:Time Adaptive Quantile Regression, Computational
Statistics and Data Analysis, Vol. 52, pp. 1292-1303, 2008.
P. Bacher, H. Madsen, H.Aa. Nielsen:Online Short-term Solar Power Forecasting, Solar Energy,
Vol. 83(10), pp. 1772-1783, 2009.
P. Pinson, H. Madsen:Ensemble-based probabilistic forecasting at Horns Rev. Wind Energy, Vol.
12(2), pp. 137-155 (special issue on Offshore Wind Energy),2009.
P. Pinson, H. Madsen:Adaptive modeling and forecasting of wind power fluctuations with
Markov-switching autoregressive models. Journal of Forecasting, 2010.
C.L. Vincent, G. Giebel, P. Pinson, H. Madsen:Resolving non-stationary spectral signals in wind
speed time-series using the Hilbert-Huang transform. Journal of Applied Meteorology and
Climatology, Vol. 49(2), pp. 253-267, 2010.
P. Pinson, P. McSharry, H. Madsen.Reliability diagrams for nonparametric density forecastsof
continuous variables: accounting for serial correlation. Quarterly Journal of the Royal
Meteorological Society, Vol. 136(646), pp. 77-90, 2010.
C. Gallego, P. Pinson, H. Madsen, A. Costa, A. Cuerva (2011).Influence of local wind speed and
direction on wind power dynamics - Application to offshore very short-term forecasting. Applied
Energy, in press
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 44
Some references (Cont.)
C.L. Vincent, P. Pinson, G. Giebel (2011).Wind fluctuations over the North Sea. International
Journal of Climatology, available online
J. Tastu, P. Pinson, E. Kotwa, H.Aa. Nielsen, H. Madsen (2011). Spatio-temporal analysis and
modeling of wind power forecast errors. Wind Energy 14(1), pp. 43-60
F. Thordarson, H.Aa. Nielsen, H. Madsen, P. Pinson (2010).Conditional weighted combination of
wind power forecasts. Wind Energy 13(8), pp. 751-763
P. Pinson, H.Aa. Nielsen, H. Madsen, G. Kariniotakis (2009). Skill forecasting from ensemble
predictions of wind power. Applied Energy 86(7-8), pp. 1326-1334.
P. Pinson, H.Aa. Nielsen, J.K. Moeller, H. Madsen, G. Kariniotakis (2007).Nonparametric
probabilistic forecasts of wind power: required properties and evaluation. Wind Energy 10(6), pp.
497-516.
T. Jónsson, P. Pinson (2010).On the market impact of wind energy forecasts. Energy Economics,
Vol. 32(2), pp. 313-320.
T. Jónsson, M. Zugno, H. Madsen, P. Pinson (2010).On the Market Impact of Wind Power
(Forecasts) - An Overview of the Effects of Large-scale Integration of Wind Power on the Electricity
Market. IAEE International Conference, Rio de Janeiro, Brazil.
Wind and Solar Power Forecasting, ESI 101 Course, July 2014 – p. 45