Energy and Power Engineering, 2013, 5, 368-372
doi:10.4236/epe.2013.54B071 Published Online July 2013 (http://www.scirp.org/journal/epe)
A Survey of Wind Power Ramp Forecasting*
Tinghui Ouyang, Xiaoming Zha, Liang Qin#
School of Electrical Engineering, Wuhan University, Wuhan, China
Received January, 2013
At home and broad, more wind power is being installed in electricity markets, the influence brought by wind power
become more important on power system stability, especially the fluctuation, the uncertainty in wind power production
and multi-time scale of the wind. In order to forecast ramp events before the power system encountering failure, so that
the operator can adopt some limited controlling strategy. This paper introduces the present status of the wind power
ramp prediction at home and abroad. And it gives out four kinds of definitions of ramp events, which are used by many
scholars, then provides various forecasting error algorithm. In the aspect of prediction models, it comes up with physi-
cal models and statistical models, and enumerates various examples of different models. Finally, it prospects the ten-
dency of the model improvement about the wind power ramp events forecasting, which would be significant for ramp
Keywords: Ramp Events; Forecasting; Physical Models; Statistical Models
It brings both opportunities and challenges after the wind
power accessing the grid. The use of fossil fuel has been
increasing for many years in the world and it generates
lots of greenhouse g ases. As a result, environmental p ol-
lution brought us a serious weather problem. In order to
reduce the environmental pollution, we should use re-
newable energy that does not pollute the environment
any more, such as wind power. Recently, wind power
energy, which is eco-friendly, is in the spotlight as the
potential energy . However, wind power brought great
threaten to the system stability because of fluctuation and
uncertainty of the wind. Especially, a typical long term
trend with large positive or negative change in a short
period, we call it a ramp. In order to get a reasonable and
efficient prediction model to forecast ramp events, each
country has done a lot of research, but so far, few can
achieve very good prediction effect.
Ramp events bring much stress to countries all over
the word. More and more countries have wind farm to
provide power output and face the ramp events at the
same time. In Europe, Denmark offers 22%, Spain sup-
plies 6%, and Germany provides 5%. In Asia, Korea an d
Japan also has wind farm, and Korea provides 83%of
total wind power generation in there . In America,
according to statistics, Texas has happened 59 ramp
events, including 35 up-ramp events and 24 down-ramp
events just in 20 05-2006, wh ich bring s great challen ge to
the regional power system's safety. Though many coun-
tries are on the wind power generation, but wind power
output is still caught short in the world. However, with
increasing wind penetration, the size of the ramp events
has also increased. For example, the installed wind ca-
pacity in the Bonneville Power Administration (BPA) is
currently over 2000 MW and is expected to increase to
over 3000 MW. At this capacity, the wind ramps can be
quite large changing by nearly 1000 MW in an hour .
So many countries studied the ramp prediction to help
the system operators make well informed scheduling
decisions and keep the generation and the load balanced.
In Australia, The Wind Power Prediction Tool (WPPT)
has been installed for th e first time, to forecast the power
output from the 65 MW Roaring 40s Renewable Energy
P/L Woolnorth Bluff Point wind farm .
In China, the wind ramp problem also exists. Since the
wind power became the world’s fastest growing energy,
China adopts the “mass (tens of KW to tens of millions
of KW) shall focus development”, “far distance high
voltage delivery” as the way of wind power development.
In 2010, the capacity of Chinese installed wind power
was 41.827 million KW, and the new installed capacity
is 16 million KW. These two both rank the first in the
world . Unfortunately, because of the high concentra-
*Project supported by the State Key Development Program for Basic
Research of China (Grant No.2012CB215101)
Copyright © 2013 SciRes. EPE
T. H. OUYANG ET AL. 369
tion of wind access in China, the fluctuation of wind
power brings the potential risk to power grid.
The first section puts forward ramp definition and pre-
diction accuracy metrics comparing with general predic-
tion. The second section summarizes various methods
used in wind power prediction and ramp forecasting. The
last section is conclusion and the tendency on wind ramp
2. Differences and Relations
2.1. Ramp Definition
Ramp prediction is a new research field and also differ-
ent from general prediction. Although it is easy to iden-
tify ramps visually, which is defined by a change in
power output that has a large enough amplitude for a
relatively short period of time, there is no consensus on
the accepted formal definition of a ramp event. But it
mainly contains two points: on the one hand is a small
time scale, which is used in most of the literature by 30
minutes, 1 hour, 4 hours; on the o ther hand is the sh arply
power change, which is generally calculated by the per-
centage of installed capacity. At present, many scholars
should take three main characteristics into account to
define a ramp event: direction, duration, and magnitude.
According to the three characteristic, different people
comes up with different definition, four kinds of which
are generally accepted by most scholars .
Definition 1: A ramp occurs when the magnitude of
the increase or decrease in the power signal in the inter-
val is greater than a predefined threshold value
Ptt PtP Δ
Definition 2: A ramp occurs when the difference be-
tween the maximum and the minimum power output
measured in that interval is greater than a threshold
value : tΔ
maxPt,ttminP t,ttP ΔΔ
Definition 3: A ramp occurs when the absolute value
of the filtered signal f
in the interval exceeds a
given threshold value : tΔ
Definition 4: A ramp occurs when the ratio between
the absolute difference of the power in the interval
is greater than a predefined reference value : tΔ
These four definitions have different applications. The
first three definitions mainly consider the change of the
amplitude, that is to say, a ramp occurs when wind
power amplitude exceeds a predefined threshold value in
a certain interval of time. The last definition uses the
wind power rate to indicate a ramp. Each of them em-
phasizes in different aspects, and has its advantages and
It is necessary to improve the definition of ramp
events according to the actual demand. Although the four
definitions can define a wind power ramp event, system
operators mainly take care of the influence after wind
power access to the electric grid. That is to say, How
seriously the wind power fluctuates can be regarded as a
quenchless ramp event for the electric power system.
Therefore, due to the actual demand, combination of grid
structure and power system operation mode is required to
further meet the need of the system when defining a
2.2. Prediction Accuracy Metrics
Ramp prediction has something to do with classical pre-
diction in the place of the prediction error. Like the clas-
sical prediction, approaches based on data mining cause
the ramp prediction to a regression problem. The output
is a real number, while the predictive accuracy is a func-
tion of the difference between the forecasted value and
the observed value . According to the conventional
wind power prediction, researchers propose many met-
rics to measure prediction accuracy. But Potter et al.
(2009) observe that such as mean square error (MSE),
root mean square error (RMSE), and other MSE-based
metrics which tend to over-penalize large errors, are not
appropriate for ramp forecasting assessment. At last,
researchers conclude three metrics, that is, the absolute
error (AE), the mean absolute error (MAE), and the
standard deviation (Std) of the absolute error [7-9].
AEty ty t
where: is the predicted value, is the ob-
served (measured) value, N is the number of test data
points for the prediction model. The difference is that
yt is the amplitude of wind power for general predic-
tion while power ramp rate (PRR) in ramp prediction.
Moreover, the data set is usually divided into training
and test data sets in ramp prediction models.
Copyright © 2013 SciRes. EPE
T. H. OUYANG ET AL.
3. Forecasting Models
For wind power forecasts, there exist many prediction
models, which might be grouped under two different ap-
proaches: Physics-based models and St atisti cal m odel s.
Physics-based models, which are based on the physi-
cal characteristics of the weather, are parametric models.
These models aim at translating and refining numerical
weather prediction (NWP) forecasts into the wind power
facilities’ sites and modeling local wind profiles. More-
over, they usually use theoretical power curve, or esti-
mated power curve, to forecast wind power output. For
example, Greaves et al. (2009) and Focken and Lange
(2008) use NWPs to produce forecasts of the power
curves of the wind generation facilities.
Statistical models are widely used forecasting models,
which use historical wind power measurements, mete-
orological data, either NWPs or historical measurements,
and machine learning algorithms to induce a predictive
model. So the following introduce some kinds of models
used in wind power ramp prediction.
3.2. Classical Prediction Models
The traditional physical models using for wind speed
forecasting and wind power predictions are based on the
weather data . They generally make use of global
databases of meteorological measurements and atmos-
pheric models. However large computational systems are
needed to calculate to achieve accurate results .
There are still many different methods for different prac-
tical problems using a physical model. For example,
computational fluid dynamics (CFD) is used as an alter-
native method to the power law to adjust for the local
conditions of the physical terrain . Model output sta-
tistics (MOS) are often used to avoid systematic fore-
casting errors and to correct the predicted power output
for unknow n s [ 13 ] .
The statistical methods forecasting the wind power
production need a vast amount of data to be analyzed and
the meteorological processes are not explicitly repre-
sented. Generally a statistical relationship is developed
between the weather forecast or prediction and the po-
tential power output from the wind farm. So the link is
determined and used to forecast the future power output.
Different from physical methods, most statistical meth-
ods involve only one-step to convert the input variables
into power output, which are called as ‘b lack box’.
There are a number of time series analysis methods
used in wind prediction, including autoregressive (AR),
moving average (MA), autoregressive moving average
model (ARMA) and autoregressive integrated moving
average model (ARIMA), the Box-Jenkins methodology,
the use of the Kalman filter and so on. But Torres et al.
 found it was possible to get 20% error reduction
compared to persistence to forecast average hourly wind
speed for a 10h forecast horizon at a number of locations
using nine years of historical data using an ARMA mod-
Some soft computing (or machine learning) ap-
proaches, as well as classical time series analysis, are
also the valid way to forecast the wind power production.
Such as artificial neural networks (ANN), fuzzy systems,
besides other models, like, gray predictors and support
vector machines (SVM) have been applied in prediction
for many years. Because they learn from the relationship
between the predicted wind and forecasted power output
using historical time series, so we call these methods
learning approaches, which are also often referred to as
artificial intelligence (AI) methods or 'gray box' methods.
Nowadays, a number of studies have already applied the
neural models to forecast wind. Welch at al  com-
pares three types of neural networks (namely MLP, si-
multaneous recurrent neural network (SRN) and Elman
recurrent neural network) trained using particle swarm
optimization (PSO) for short-term prediction of wind
Recently, Wind speed and power output were fore-
casted with an accuracy respectively 11.2% and 12.2%
better than persistence in terms of MAE by using a grey
predictor with a look-ahead time of 1h . On the other
hand, using a genetic algo rithm (GA) to optimize a fuzzy
inference system (FIS) model as an improvement, the
result was between 9.5% and 28.4% over persistence
depending on the forecast horizon . And researchers
have started to use decision tree techniques in data min-
ing . The results indicate that the predictive power of
individual variables is dependent on the seasons. And
comparing wind power forecasts at 10 wind farms to the
NWP data at each wind farm by using classical MLP
ANNs, mixture of experts, SVM and nearest neighbor
with PSO , the main conclusion is that combining
several models for day-ahead forecasts produces better
results. For example, Mohandes et al.  compared
SVM to a multi-layer perceptron ANN model to predict
wind speed. Negnevitsky et al. combine two AI me-
thods, ANN and fuzzy logic in a hybrid approach to de-
velop an adaptive neural fuzzy system model (ANFIS).
3.3. Ramp Prediction Models
Time series analysis is also an effective method applied
to ramp prediction. Because wind power data used in
ramp prediction are also time series data which is ob-
served at regular intervals such as year, month, day and
hour etc.. So time series analysis can forecast the
future value using the past data. Many ramp prediction
algorithms have been proposed such as ARIMA model,
regression analysis method, moving average method,
Copyright © 2013 SciRes. EPE
T. H. OUYANG ET AL. 371
exponential smoothing method, and decomposition
method, etc. Reference  forecast power output with
ARIMA and exponential smoothing method that are in-
variant time series models. Reference  considers the
seasonal factors, and concludes that up ramp tend to oc-
cur during the mid-day and afternoon periods and down
ramp tend to occurs during the evening and night times.
However, wind power has uncertainty in multiple time
scales, each requires its own time series modeling ap-
proach. In the range of seconds to minutes, autoregres-
sive or persistence techniques can deal well with small
amounts of fast fluctuations, but badly with longer term
trends. ARIMA model can process an integral step, but
do not capture abrupt changes in wind power ramp.
To forecast the ramp events, most of the existing wind
power forecasting methods are not suitable. They are
based on the “point forecasting”, i.e., forecasting the
exact value of wind power at a future time[23-25].Some
other methods have extended the point forecasting me-
thods by estimating the confidence interval of point
forecasts. No matter what model is used, it is about
history data point and future point forecast. The differ-
ence between all of the methods is the selection of time
interval, usually for a few minutes ahead to 24 hours
ahead. As a result, forecasting still suffer from a high
level of inaccuracy because ramp event represent a scene
not a point. So some people categorize ramp events into
'classes', and come up with SVM as classifiers and an
elaborate model, which can use available data to predict
the class of future ramps. Reference  uses SVM as
the classification engine to predict ramp event. Further-
more, it used the One-Against-All approach to extend
and apply the binary SVM to multi-class prob lem.
As is said above, ramp forecasting is a kind of scene
prediction which can use Markov chain. Most described
models rely on an observable process and are determined
as a function of past values of the process. Markov
Switching Auto Regressive (MSAR) models, which al-
low the switches to be governed by an unobservable
process, propose an alternative to this observable re-
gime-switching modeling. It is assumed to be a Markov
chain. A good characteristic of such approach is that
permits to reflect the impact of some external factors on
the behaviors of certain time-series . Because it can
manage to capture the influence of some complex mete-
orological features, Markov chain is found to be suitable
for modeling especially for weather variables, such as
daily rainfall occurrences  or wind fields . For
the specific case of the fluctuations of wind generation,
some people use this hidden Markov Models (HMM) to
describe meteorological features and to forecast the wind
ramp scene that cannot be determined from past values
of measured power production only. HMM models and
the estimation of their parameters are briefly described in
To improve the ramp prediction approach, combina-
tions of different methods are necessary. There are many
methods existing to forecast wind ramp, most of which
are inherited from the classical wind power prediction
methods. However, each has its own application field.
Generally speaking, at home and abroad, few of them
can acquire high accuracy in forecasting a wind power
ramp. Comprehensive consideration, on the one hand,
time series models, a kind of statistical prediction meth-
ods, have advantages in dealing with the past historical
data to predict future data. On the other hand, Markov
chain has its unique advantages at the transformation
between different events. So we speculate that combin-
ing time series with HMM models would achieve better
results in the wind ramp forecasting.
4. Conclusions and Tendency
It is necessary to improve forecasting accuracy of the
wind power ramp. With the increasing of wind power
installed capacity and concentrating arrangement of most
wind farms, the wind ramp models are needed to be im-
proved so that system operators can ensure power system
safety and economy. The following gives out some sug-
gestions to improve the results of ramp prediction :
1) Physics-based models mainly rely on NWP data.
Because of NWP data coming from signal model, low
update frequency and resulting in forecasting errors,
these make the accuracy of wind power ramp prediction
low. Combining with multiple NWP models can improve
Physics-based modeling, which can improve the accu-
racy of wind power ramp prediction.
2) For statistical model, the domestic and foreign
scholars have studied that combining the prediction re-
sults of different forecast methods can further improve
the prediction accuracy. Moreover, using some good data
mining methods to process data can also improve the
3) Furthermore, the combination and optimization of
the prediction results from physical models and statistical
models can also improve the accuracy.
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