CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING …shodhganga.inflibnet.ac.in/bitstream/10603/23474/6/07.chapter 5.pdf · Wind power prediction based on numerical and statistical

CHAPTER 5 – DEVELOPMENT OF WIND POWER FORECASTING MODELS 122

CHAPTER 5

DEVELOPMENT OF WIND POWER FORECASTING

MODELS

The models proposed for wind farm power prediction have been dealt with

in this chapter. Accurate prediction of wind farm power is essential for increasing

wind penetration in the electricity grid. It also aids the power system operators in

planning unit commitment, economic scheduling and dispatch. Reliable wind power

forecasts help the wind farm owners and electricity traders to plan accordingly and gain

maximum profits.

Wind power prediction based on numerical and statistical models have been

developed by Stathopoulos et al (2013). They concluded that accurate power

prediction is possible if the local atmospheric conditions are estimated correctly.

Sideratos and Hatziargyriou (2012) developed models for wind power forecasting with

a focus on extreme power systems events.

Part of the work reported in this chapter has been submitted for Review as under:

Lydia. M., Suresh Kumar. S., Immanuel Selvakumar. A. and G. Edwin Prem Kumar, “Wind

Farm Power Prediction Based on Wind Speed and Power Curve Models”, Elsevier -Journal of

Applied Energy


Bessa, Miranda and Gamma (2009) developed wind power forecasting

models adopting the concept of entropy. Kusiak and Li (2010) developed a short-term

prediction model for power produced by wind turbines at low wind speeds using

clustering approach. A new multivariate Least Squares – SVM model for wind power

forecasting has been presented by Wang et al. (2012).

The important contributions of this chapter include the description of

methodology and performance of

Direct Wind Power Forecasting Model for Multi-Step Forecast

o NAR models based on time-series wind power data with and without

exogenous variables

Combined Wind Power Forecasting Model for Multi-Step Forecast

o NAR based wind speed forecasting model with and without exogenous

variables combined with parametric and non-parametric models of wind

power

5.1 PROPOSED MODELS FOR WIND POWER FORECASTING

The proposed Direct and Combined Model for wind power forecasting

has been discussed in detail in this section.

5.1.1 Proposed Direct Models

Direct models for wind power forecasting, predict the wind power using the

historic time series wind power data. The direct models for wind power prediction are

based on NAR models with and without exogenous variables (Figure 5.1). WP11 refers

to the 10-min averaged wind power data of December 2011.


The Non-Linear Auto Regressive model for wind power (NAR 2) is defined

by Equation (5.1)

))5(),4(),3(),2(),1(()( tPtPtPtPtPftP -----(5.1)

where P(t) is the wind power at time instant t. The model inputs are determined after

application of feature selection algorithm.

Fig. 5.1 Proposed Direct Models for Wind Power Forecasting

The Non-Linear Auto Regressive model for wind power (NARX 4) is

developed with wind speed as exogenous variable. Using the sequential feature

selection algorithm, the model is defined by Equation (5.2)

))4(),2(),1(),5(),4(),3(),2(),1(()( tytytytPtPtPtPtPftP -----(5.2)

5.1.2 Proposed Combined Models

The schematic diagram of the combined wind power forecasting model is

shown in Figure 5.2. It consists of the combination of a wind speed prediction model

and wind turbine power curve model. Time-series wind speed forecasting models have

been developed for 10-min ahead prediction using data mining algorithms.

•NAR2 (WP11)

•NARX4 (WP11+WS11)

Wind Power Forecasting

Models

•SMOreg

•MLP

•Bagging

•M5R

Algorithms


Fig. 5.2 Proposed Combined Models for Wind Power Forecasting

Wind turbine power curve models developed in Chapter 4 have been used to

model the wind turbine power curve. The wind farm power output is predicted using

this combined model. The output of the best performing wind speed model is given as

input to the best performing wind turbine power curve model. Multi-step prediction

models for the next six consecutive time steps have been developed for wind speed.

Wind power prediction models can be developed either using the historic wind power

data or wind speed data along with exogenous variables. Development of wind speed

forecasting models based on nonlinear auto regressive models with and without

exogenous variables using data mining algorithms is a novel work.

Wind turbine power curve models based on four and five parameter logistic

expressions, with their parameters solved using PSO and DE were found to give better

results than the other algorithms in Chapter 2 and Chapter 4. Multi-step prediction of

wind power using the best performing wind turbine power curve model has been

developed with an aim to provide very short-term wind power forecasting. The multi-

step wind speed prediction models will be the input to the multi-step power prediction

models at the corresponding time steps. Hence at any particular instant, the combined

wind power prediction model, will give the power output of next six consecutive time

steps.

Wind Turbine

Power Curve

Model

Actual

wind

speed

data

Predicted wind

speed data Actual

Wind

Speed Data

Wind Power

Forecast Wind Speed

Prediction

Model


Wind Speed Forecasting Model for Proposed Combined Model

Four different time-series models have been developed for 10-min ahead

wind speed forecasting. Figure 5.3 gives a schematic representation of the four models

developed for forecasting wind speed. WS11, WS10, WS09 refers to the 10-min

averaged wind speed data of December in the years 2011, 2010 and 2009 respectively

and WD represents wind direction. The linear and non-linear models developed in

Chapter 3 cannot be used here since large number of wind speed data is required to

predict the corresponding wind power using the WTPC. Models for multi-step

prediction up to six consecutive time steps have also been developed.

Fig. 5.3 Wind Speed Forecasting Models for Proposed Combined Model

The first wind speed forecasting model is a Non-Linear Auto Regressive

model (NAR1) and the remaining models are developed using exogenous variables,

namely the wind direction and wind speed of the previous two years. These non-linear

models are solved using data mining algorithms namely SMOreg, Lazy k-star, bagging,

M5R and M5P.

•NAR1 (WS11)

•NARX1 (WS11+WD)

•NARX2 (WS11,WD, WS10)

•NARX3 (WS11,WD, WS10, WS09)

Wind Speed Forecasting Models

•SMOreg

•Bagging

•M5R

•M5P

Algorithms


NAR Models for Wind Speed

The NAR models do not have any exogenous input signal. The expression

for the NAR model developed for wind speed (NAR 1) using the sequential feature

selection algorithm is given in Equation (5.3).

))5(),3(),2(),1(()( tytytytyfty -----(5.3)

NARX Models for Wind Speed

The wind direction and wind speeds of the corresponding time in the

previous two years are the exogenous variables that have been taken into consideration.

1) Model Incorporating Wind Direction

Sensing wind direction is essential to capture maximum power from the

wind. It is usually measured in cardinal directions or in azimuth degrees and is

measured using a wind vane. The non-linear autoregressive model including wind

direction (NARX 1), using the sequential feature selection algorithm is given in

Equation (5.4)

))3(),1(),5(),3(),2(),1(()( 11 tututytytytyfty -----(5.4)

where u1 is the wind direction of the corresponding period.

2) Model Incorporating Annual Trends

A linear time series based model for forecasting wind speed and direction

was proposed by relating the predicted interval to its corresponding one and two year

old data (El-Fouly et al. 2008). The one year model performed better than the two

years model for smaller prediction horizons, while the two years models performed


better for larger prediction horizons. The non-linear autoregressive model with wind

direction and wind speed of the corresponding period in the previous year (u2) as

exogenous variables (NARX 2), after application of feature selection algorithm is

defined in Equation (5.5).

))4(),3(),1(),5(),3(),2(),1(()( 211 tutututytytytyfty -----(5.5)

The non-linear autoregressive model with wind direction and wind speed of

the corresponding period one year before and two years before (u3) as exogenous

variables (NARX 3), after application of feature selection algorithm is defined in

Equation (5.6)

))7(),1(),4(),3(),1(),5(),3(),2(),1(()( 33211 tutututututytytytyfty

-----(5.6)

Wind Turbine Power Curve Models for Combined Model

The modeling requirement, objectives and techniques involved in developing a

wind turbine power curve has been dealt in Chapter 4. The wind turbine power curve

models and modeling techniques used in developing the combined model of wind

power prediction is shown in Figure 5.4.

Fig. 5.4 WTPC Models for Proposed Combined Model

•Parametric Models

•Four parameter logistic expression

•Five parameter logistic expression

•Non-parametric models

WTPC Models

•PSO

•DE

•MLP

•BAGGING

•M5R

•M5P

Algorithms


5.2 MODELING TECHNIQUES FOR PROPOSED MODELS

In order to develop the direct and combined models of wind power

forecasting, the performance of several data mining algorithms was evaluated. The

best four algorithms were chosen to develop the models. The direct models for wind

power forecasting are developed using four data mining algorithms namely SMOreg,

Bagging, MLP and M5R.

In the Combined Model for wind power forecasting, the wind speed

forecasting models are realized using SMOreg, Bagging, M5R and M5P. The

parametric models of the WTPC have been developed using four and five parameter

logistic expressions solved using PSO and DE. The non-parametric models of the

WTPC are realized using MLP, Bagging, M5R and M5P. All these techniques have

been discussed in detail in Chapters 3 and 4.

5.3 RESULTS AND ANALYSIS

The data used for this research, the results obtained and the analysis of

results have been presented in this section. The best performing model is chosen based

on least value of MAE and RMSE compared to other models used.

5.3.1 Experimental Data

The real-time data (Dataset 3) used for development of wind speed and

wind power prediction models was obtained from Sotavento Galicia Plc., an

experimental wind farm supported by the “Xunta de Galicia”, the regional autonomous

government. The 10-minutes averaged data for the month of December 2011 has been

used for the combined wind power prediction models (Figure 5.5). The total number


of data is 4446, half of which was used for training and another half was used for

testing the data mining algorithms. The parametric algorithms which involved the

application of optimization techniques were developed using the entire set of data.

Fig. 5.5 Real-time Data of Sotavento Wind Farm - December 2011

5.3.2 Results of Proposed Direct Wind Power Forecasting Models

The performance of the non-linear autoregressive models for wind power

with and without external variable developed using four different data mining

algorithms has been tabulated in Table 5.1. The time series wind power model

developed using SMOreg algorithm gives better performance and is followed by the

M5R algorithm. The multi-step prediction models for the direct models of wind power

have been developed using the SMOreg algorithm and their performance is presented

in Table 5.2.

Table 5.1 Performance of Proposed Direct Models for Wind Power

NAR2 NARX4

MAE RMSE MAE RMSE

SMOreg 8.043 14.0117 8.0402 14.0155

MLP 9.6681 14.8937 10.6489 15.3922

BAGGING 8.5042 14.6322 8.536 14.6798

M5R 8.5013 14.0618 8.5147 14.0218

0 5 10 15 20 25 30 350

50

100

150

200

250

300

350

400

450

500

Wind Speed (m/s)

Win

d P

ow

er

(kW

)


Table 5.2 Performance of Proposed Direct Models for Multi-Step Prediction

NAR2

(SMOreg)

NARX4

(SMOreg)

1st time step

MAE 12.4829 12.4683

RMSE 20.4063 20.4038

2nd

time step MAE 15.1658 15.1424

RMSE 24.2292 24.2641

3rd

time step MAE 17.1724 17.1575

RMSE 27.0302 27.1025

4th

time step MAE 18.8682 18.8556

RMSE 29.1639 29.2155

5th

time step MAE 20.3377 20.3264

RMSE 31.0748 31.1524

6th

time step MAE 21.3629 21.3559

RMSE 32.4438 32.5255

It can be observed from Table 5.2 that based on MAE measure, the model

NARX4 developed using SMOreg performs better for multi-step prediction and the

model NAR2 developed using SMOreg performs better when the RMSE measure is

considered. Monteiro et al (2009) state that the choice between MAE and RMSE as

main evaluation criterion for wind forecasting models depends on the end-users’

sensitivity to the errors, which is represented by the loss function. If a quadratic loss

function is used in an algorithm, RMSE is the best error measure and if a linear loss

function is used, MAE is the best. If the loss function representing the sensitivity of

forecast users is not clearly defined, MAE is the preferred criterion.

5.3.3 Results of Proposed Combined Wind Power Forecasting Models

As the combined model for wind power prediction is developed using the

best performing wind speed model and wind power curve model, the performance of

the various models developed in this regard have been analyzed in the following

section. The performance of these parametric and non-parametric models has been

evaluated using the metrics Mean Absolute Error (MAE) and Root Mean Squared

Error (RMSE).


Performance of Proposed Wind Speed Prediction Models

The performance of the time-series models for wind speed developed using data

mining algorithms has been tabulated in Table 5.3.

Table 5.3 Performance of Proposed Time Series Models for Wind Speed

Forecasting

NAR1 NARX1 NARX2 NARX3

MAE RMSE MAE RMSE MAE RMSE MAE RMSE

SMOreg 0.4984 1.0216 0.5095 1.0256 0.5075 1.0248 0.5253 1.033

BAGGING 0.5198 0.932 0.5306 0.9372 0.5282 0.9368 0.5295 0.9352

M5R 0.4947 0.9025 0.5108 1.1328 0.512 1.1354 0.5145 1.1413

M5P 0.504 0.9246 0.5021 0.9831 0.5025 0.9782 0.5043 0.9786

In order to ascertain the best wind speed forecasting model, the model with

least error metrics among the four is chosen and the best algorithm that can be used to

realize it also needs to be chosen. Hence, the wind speed forecasting model with the

best MAE and RMSE measure realized using various algorithms are considered.

The M5R algorithm performs best for the non-linear autoregressive model

(NAR1) for wind speed. The M5P and bagging algorithms gives the lowest MAE and

RMSE respectively for all the other models that are developed including the different

external variables. The multi-step prediction models have been developed for wind

speed using these best performing algorithms and their performance is presented in

Table 5.4.


Table 5.4 Performance of Multi-Step Wind Speed Prediction Models

1st time step 2nd time step 3rd time step 4th time step 5th time step 6th time step

MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE

NAR1

(M5R) 0.6892 1.1433 0.788 1.2397 0.8632 1.3137 0.9243 1.3701 0.9762 1.4239 1.0322 1.4806

NARX1

(BAG) 0.7266 1.1734 0.8558 1.2965 0.9423 1.3728 1.0088 1.4298 1.0628 1.4816 1.114 1.5415

NARX1

(M5P) 0.6832 1.1395 0.7843 1.2355 0.8568 1.311 0.9236 1.3815 0.9794 1.4488 1.0332 1.5107

NARX2

(BAG) 0.7212 1.1688 0.8444 1.2887 0.9268 1.3661 0.9939 1.4226 1.0477 1.4769 1.0952 1.5318

NARX2

(M5P) 0.6833 1.1384 0.7838 1.2369 0.8584 1.3178 0.9252 1.4009 0.9801 1.4595 1.0336 1.5323

NARX3

(BAG) 0.7293 1.1777 0.8462 1.2965 0.9334 1.3745 1.007 1.4326 1.0637 1.4877 1.1137 1.5423

NARX3

(M5P) 0.6855 1.1409 0.7878 1.2456 0.8625 1.3333 0.9253 1.3887 0.9852 1.475 1.0251 1.488

The results for multi-step prediction throws light how a model would

behave for hourly forecasts. It can be inferred from Table 5.4 that based on RMSE

measure the NARX models perform better than NAR model for the first three time-

steps and for the last three time-steps the NAR model outperforms the NARX models.

If only the NARX models are considered, the NARX1 model developed using wind

direction as the only exogenous variable realized using M5P algorithm performs better

than all other NARX models for five consecutive time-steps.

Performance of Proposed Wind Power Prediction Models

In order to identify the wind power prediction model with least error

metrics, the best performing model and the best algorithm to develop the model needs

to be ascertained. Hence the parametric and non-parametric wind turbine power curve

models as discussed in section 5.2.2 were developed. The data mining algorithms used

for non-parametric models were developed and tested in WEKA. The optimization

techniques, PSO and DE, used for solving the logistic expressions of the parametric


models were developed in MATLAB. The population size and maximum number of

iterations of PSO and DE and their control parameters are same as that specified in

Chapter 4.The best performing wind speed prediction models are given as input to the

wind power curve models (Figure 5.6). The term 4P refers to the four parameter

logistic expression and 5P refers to five parameter logistic expression. The

performance of these combined models of wind power prediction is presented in Table

5.5.

Fig. 5.6 Combined Wind Power Prediction Modeling Techniques

Four parametric models and four non-parametric models of WTPC have

been developed. Table 5.5 clearly shows that the parametric power curve models

totally outperform the non-parametric models. The wind turbine power curve

developed using five parameter logistic expression whose parameters are optimized

using PSO gives best results for all the NARX models of wind speed. Parametric

models solved using DE algorithm gives the best performance for the NAR model of

wind speed.

•NAR1(M5R)

•NARX1 (M5P)

•NARX1 (Bagging)

•NARX2 (M5P)

•NARX2 (Bagging)

•NARX3 (M5P)

•NARX3 (Bagging)

Wind Speed Forecasting Models

•4P (PSO)

•4P (DE)

•5P (PSO)

•5P (DE)

•MLP

•BAGGING

•M5R

•M5P

WTPC models


Table 5.5 Performance of Proposed Combined Models of Wind Power

PSO (4P) DE (4P) PSO (5P) DE(5P) MLP BAGGING M5R M5P

NAR1

(M5R)

MAE 18.9513 18.1798 18.0119 17.976 19.6942 20.0661 20.3686 20.129

RMSE 28.4947 28.2413 28.3261 28.2669 28.6915 30.1371 30.0941 30.0815

NARX1

(BAG)

MAE 19.2134 18.5881 18.5114 18.5606 20.353 20.8368 21.2105 20.9252

RMSE 29.4318 29.4823 29.7145 29.7902 30.0944 32.0502 32.1633 32.1388

NARX1

(M5P)

MAE 18.8964 18.1607 17.9884 18.0204 19.744 20.2481 20.5261 20.2989

RMSE 29.1229 28.8588 29.0041 29.0366 29.0413 30.3716 30.3135 30.3534

NARX2

(BAG)

MAE 19.266 18.6231 18.5591 18.612 20.4419 20.8787 21.3355 21.0462

RMSE 29.5717 29.636 29.8641 29.9418 30.2651 32.1964 32.3722 32.3208

NARX2

(M5P)

MAE 18.8749 18.1313 17.9514 17.9825 19.695 20.2229 20.5157 20.2799

RMSE 29.0591 28.7408 28.871 28.8968 28.8868 30.635 30.491 30.4906

NARX3

(BAG)

MAE 19.2811 18.6657 18.5523 18.6008 20.4208 20.9263 21.2327 20.9401

RMSE 29.6542 29.686 29.8898 29.9654 30.1992 32.2663 32.2856 32.2313

NARX3

(M5P)

MAE 18.8629 18.1063 17.924 17.9563 19.6744 20.2712 20.4861 20.249

RMSE 29.0682 28.7092 28.8127 28.8358 28.8076 30.6233 30.4985 30.4797

Multi-step prediction models of wind power are developed for the best

combination of wind speed and power and their performance is tabulated in Table 5.6.

It can be observed that the combination of non-linear autoregressive wind speed model

without any external variables (NAR1) developed using M5R algorithm and wind

turbine power curve model developed using four parameter logistic expression solved

by DE algorithm records the best RMSE value for most of the time-steps.

5.3.4 Analysis of Results

The multi-step prediction of the combined models of wind power has been

analyzed in Table 5.7 using two criteria, mean and standard deviation (Std) of the

errors. The mean value gives an idea about the error magnitude in every consecutive


step and Std is a measure of how spread out the values are. The combination of non-

linear autoregressive wind speed model without any external variables (NAR1)

developed using M5R algorithm and wind turbine power curve model developed using

four parameter logistic expression solved by DE algorithm registers lowest mean and

standard deviation when the RMSE is considered. Though the mean of its MAE errors

are higher than the other models, its standard deviation is the lowest. This model

which has the lowest standard deviation augurs well for multi-step prediction.

Prediction of wind power based on this model would definitely give a very reliable

result for very short term horizon spanning from 10 minutes to 1 hour.

Table 5.6 Performance of Multi-step Prediction of Proposed Combined Models

WS

model

NAR1

(M5R)

NAR1

(M5R)

NARX1

(M5P)

NARX1

(M5P)

NARX2

(M5P)

NARX2

(M5P)

NARX3

(M5P)

NARX3

(M5P)

WP

model DE(4P) DE(5P) DE(4P) PSO(5P) DE(4P) PSO(5P) DE(4P) PSO(5P)

1st time

step

MAE 20.7971 20.6379 20.5797 20.4825 20.5343 20.4323 20.5491 20.4407

RMSE 32.3466 32.5559 32.6416 32.8376 32.4872 32.6725 32.4585 32.6198

2nd

time

step

MAE 22.5446 22.4762 22.0703 22.0046 22.0344 21.9560 22.0813 21.9897

RMSE 34.5132 34.8309 33.8235 33.9954 33.8607 34.0038 34.0114 34.1267

3rd

time

step

MAE 24.0281 24.0235 23.9381 23.9253 23.9176 23.8909 23.9530 23.9258

RMSE 36.3888 36.7815 36.6484 36.8099 36.7724 36.9071 36.8793 37.0102

4th

time

step

MAE 25.4151 25.4377 25.3981 25.4329 25.3087 25.3378 25.2108 25.2568

RMSE 38.1803 38.5999 38.4410 38.6175 38.5202 38.6891 38.3307 38.5526

5th

time

step

MAE 26.6340 26.6208 26.7813 26.8438 26.5982 26.6718 26.4239 26.5089

RMSE 39.5764 40.0281 40.1185 40.3744 40.0322 40.3333 39.5346 39.8296

6th

time

step

MAE 27.5459 27.5661 27.7663 27.8824 27.5451 27.6744 27.4879 27.6430

RMSE 40.8588 41.3502 41.4779 41.7974 41.2307 41.5826 40.9997 41.4174


Table 5.7 Analysis of Multi-Step Prediction of Proposed Models of Wind Power

Combined Models of

Wind Power Prediction

MAE RMSE

Mean Std Mean Std

NAR1(M5R)+DE(4P) 24.49 2.547 36.98 3.198

NAR1(M5R)+DE(5P) 24.46 2.608 37.36 3.298

NARX1(M5P)+DE(4P) 24.42 2.765 37.19 3.488

NARX1(M5P)+PSO(5P) 24.43 2.845 37.41 3.533

NARX2(M5P)+DE(4P) 24.32 2.694 37.15 3.451

NARX2(M5P)+PSO(5P) 24.33 2.782 37.36 3.518

NARX3(M5P)+DE(4P) 24.28 2.631 37.04 3.279

NARX3(M5P)+PSO(5P) 24.29 2.733 37.26 3.375

NAR2 +SMOreg 17.56 3.333 27.39 4.498

NARX4+SMOreg 17.55 3.333 27.44 4.528

5.4 WIND RESOURCE ESTIMATION

Wind resource estimation has been done as an application of the developed

wind farm power forecasting model. Estimation of wind resource in a given area has

several advantages. It helps to identify potential sites for wind farm establishment and

aids in the calculation of annual energy produced. Estimation of annual energy is

helpful in improving the penetration of wind power in the electricity grid and also in

electricity trading. In this research work, wind resource estimation has been carried out

for Sulur, a town in Tamil Nadu, India, a potential area for wind farm development.

This has been done using wind speed forecasting models and wind turbine power curve

models.

Wind resource estimation is the essential prerequisite for identifying

potential wind farm sites both onshore and offshore. Accurate estimates of wind

energy can revolutionize electricity markets and go a long way in transforming wind


farms to wind power plants. Estimates of wind energy will also help the wind farm

owners to choose the ratings of wind turbines to be installed. Wind resource is defined

as the actual long-term kinetic energy content of the wind at specific height and

location. An overview of the various methods used to estimate wind resource at a

particular site has been presented by Landberg et al (2003). Eight different methods of

wind resource assessment has been outlined namely, folklore, measurements only,

measure-correlate-predict (MCP), global databases, wind atlas methodology, site data-

based modeling, mesoscale modeling and combined meso/microscale modeling.

Complex terrain, offshore sites, high elevation, forest sites etc are the few challenges

faced by wind resource estimation techniques.

A brief survey of the various research works that are going on in the field of

wind resource assessment has been presented here. An analytical predictive model that

could be used for carrying out a pre-assessment study of a potential site for wind farm

establishment has been developed by Ajayi et al. (2012). This model could be used by

wind farm investors to identify potential sites for wind farm and also to assess the wind

energy that could be generated. The model was found to outperform the conventional

Weibull statistics model. The Annual Energy Production (AEP) of a potential wind

farm site has been estimated using Bayesian approach (Jung et al. 2013). The approach

effectively addresses the uncertainties that exist due to limited availability of data and

the inherent uncertainty in wind speed, air density, surface roughness exponent and

power performance of the turbine. The wind energy potential of a site has been

predicted using weighted error functions in artificial neural networks (Jung and Kwon,

2013). The frequency of wind speed and the power performance curve has been used

to develop the weighted form of the error function.

Forecasting of wind energy using automatic tuning of Kalman filters by

maximum likelihood methods has been developed by Poncela et al. (2013). New


multivariate Kalman filters have been used to forecast wind power and the model

parameters are automatically optimized through site-dependent fine-tuning. Celik and

Kolhe developed generalized feed-forward neural networks to predict an annual wind

speed probability density distribution. This approach uses the same input parameters

as the Weibull function and is observed to give better results for energy output

calculations. Lim and Jeong (2010) estimated the wind energy potential of the Wol-

Ryong coastal region. The power spectrum analysis was conducted on the horizontal

and vertical wind speed over a wide range of frequencies to ascertain a potential site

for wind farm. The wind and wave energy resources along the Caspian Sea have been

evaluated by Rusu and Onea (2013).

The seasonal and spatial distributions of the wind energy have evaluated

based on the power estimated to be delivered by Siemens 2.3 wind turbines. An

analysis of wind climate features of three regions in Turkey and the estimation of their

wind energy potential have been presented by Onat and Ersoz (2011). A five-layer

Sugeno type ANFIS model has been used to determine the relationship between wind

speed and other climatic variables. The wind energy potential was estimated using the

WASP software.

The assessment of wind energy potential at Kudat and Labuan has been

carried out using two-parameter Weibull distribution by Islam et al (2011). The spatial

distribution of high altitude wind energy potential has been estimated for Southeast

Europe by Ban et al. (2013). High altitude winds along with solar energy, is considered

to be a promising source of renewable energy in the near future. An assessment of

wind energy potential in Tehran, as a source of power generation both for grid-

connected and stand-alone operations has been carried out by Keyhani et al. (2010).

The Weibull parameters and meteorological data of about eleven years have been used

in this research. An assessment of wind energy potential at an offshore demonstration


wind farm in Korea has been presented by Oh et al. (2012). Seasonal and diurnal

changes in wind speed have been analyzed and the long term wind potential has been

estimated using the MCP method. Wu, Wand and Chi (2013) performed the wind

energy assessment based on three probability density functions namely two-parameter

Weibull, Logistic and Lognormal functions. Among the three, the Logistic function

provided a better result for wind speed distribution modeling.

In this research work, wind resource estimation has been performed based

on wind speed and power curve models (Fig. 5.7). The predicted wind speed is

extrapolated to three different heights namely 50m, 80m and 100m. The wind turbine

power data is statistically generated and the power curve is modeled using parametric

and non-parametric techniques. The AEP for the site under study has been calculated.

Fig. 5.7 Wind Resource Estimation Methodology

5.4.1 Requirements for Wind Resource Estimation

The significance of the selected site, the extrapolation of wind speed and the

calculation of AEP have been discussed here.

Wind Turbine

Power Curve

Model

Predicted Wind

Speed Wind Resource

Estimation Wind Speed

Extrapolation

Wind Speed

Prediction

Model


Site Selection

Sulur is a place Sulur is a place located in Coimbatore district of Tamil

Nadu, India (Figure 5.8). Sulur is located at 11.03N and 77.13 E

(http://www.fallingrain.com/world/IN/25/Sulur.html). It has an average elevation of

339m. It is popular location for textile and weaving mills. There is also an air force

base operated by the Indian Air Force near Sulur.

Fig. 5.8 Geographical Location of Sulur

The variation of wind speed in Sulur region between June 2011 to May

2012 is shown in Fig. 5.9. The wind speed is measured at a height of 2m from the

ground level at TAWN for agricultural purposes. Hence the wind speed is extrapolated

to 50m, 80m and 100m using appropriate surface roughness factor.


Fig. 5.9 Weibull Probability Density Function of Wind Speed in Sulur

(June 2011-May 2012)

Annual Energy Production

The annual energy produced is calculated using Equation (5.7)

)(

1

i

N

i

yPAEP

-----(5.7)

Where N is the total number of hours in a year, y is the wind speed and P is the average

hourly power output. The power output is calculated from wind turbine power curve

model which is developed using various parametric and non-parametric techniques.

5.4.2 Proposed Models for Wind Resource Estimation

The wind speed predicted by the time-series wind speed forecasting model

proposed in Chapter 3 is used. The wind power data is statistically generated and the

wind turbine power curve model developed using the four parameter logistic

expression solved using DE as discussed in Chapter 4 has been used. Fig. 5.10 shows

the model wind turbine power curve used to estimate the wind power at the

corresponding predicted wind speed values. The models and the modeling techniques

have been dealt in detail in the previous chapters.

0 5 10 15 20 25 30 350

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Wind Speed (kmph)

Pro

ba

bili

ty

Sulur


Fig. 5.10 Model Wind Turbine Power Curve

5.4.3 Results and Analysis

The performance of the wind speed and wind turbine power curve models

has been measured using the Mean Absolute Error (MAE) and Root Mean Squared

Error (RMSE) as performance metrics. The extrapolated wind speed and the energy

estimated at 50m, 80m and 100m are shown in Fig. 5.11 and Fig. 5.12 respectively.

The annual energy estimated for June 2011 to May 2012 is tabulated in Table 5.8.

Fig. 5.11 Extrapolated Wind Speed Data of Sulur

0 2 4 6 8 10 12 14 16 180

5

10

15

20

25

Wind Speed (m/s)

Win

d P

ow

er

(kW

)

Actual Power

Estimated Power

June 2011 July 2011 Aug 2011 Sept 2011 Oct 2011 Nov 2011 Dec 2011 Jan 2012 Feb 2012 Mar 2012 Apr 2012 May 20121

2

3

4

5

6

7

8

Win

d S

pe

ed

(m

/s)

z =2m

z = 50m

z = 80m

z = 100m


Fig. 5.12 Estimated Energy Production in Sulur

Table 5.8 Annual Energy Estimated in Sulur region

Height

(m)

Annual Energy Estimated

(MWh)

50 42.679

80 47.545

100 49.839

5.5 SUMMARY

Wind power prediction models can revolutionize electricity trading, aid the

power system operators in planning and control. The findings in this chapter can be

summarized as below:

Though the direct models of wind power prediction perform better than the

combined model, the need for the combined models for wind farm power prediction is

justified by the facts that wind speed data is more commonly available than wind

power data.

The combined models of wind power prove useful, when the wind resource

potential of a particular site is needs to be established.

Combined power prediction models consisting of wind speed and wind turbine

power curve models have been developed.

June 2011 July 2011 Aug 2011 Sept 2011 Oct 2011 Nov 2011 Dec 2011 Jan 2012 Feb 2012 Mar 2012 Apr 2012 May 20120

2

4

6

8

10

12

14

En

erg

y E

stim

ate

d (

MW

h)

z = 50m

z = 80m

z = 100m


The combination of non-linear auto regressive wind speed model developed

using M5R algorithm together with the wind turbine power curve model developed

using the four parameter logistic expression, whose parameters were solved using DE,

performed best.

The multi-step prediction of this combined model of wind power is very

impressive as it records the lowest standard deviation for both the error measures

namely MAE and RMSE. These models can be effectively used to predict power for

consecutive time steps, using wind speed as the only input. Development and

implementation of this model will definitely go a long way in making wind generated

power more attractive, reliable and competitive.

The application of wind speed forecasting models and wind turbine power

curve models has been proposed for wind resource estimation at Sulur, Tamil Nadu,

India.

The time series model for wind speed of one year was used to predict the wind

speed of the next year. The wind speed was extrapolated to three different

heights namely z = 50m, z = 80m and z = 100m.

The wind turbine power curve modeled based on four parameter logistic

expression solved using DE was used to predict power.

The AEP at the site under study has been estimated at three different heights.

Accurate models of wind resource estimation are the need of the hour to

identify potential wind farm sites, both onshore and offshore. It can make the wind

resource more reliable and hence enhance the penetration of wind power in electricity

grids. It can also have significant impact in the electricity markets and transform wind

farms into wind power plants.

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