CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS

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1 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

2 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 123 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 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.

3 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 124 The Non-Linear Auto Regressive model for wind power (NAR 2) is defined by Equation (5.1) P ( t) f ( P( t 1), P( t 2), P( t 3), P( t 4), P( t 5)) -----(5.1) where P(t) is the wind power at time instant t. The model inputs are determined after application of feature selection algorithm. NAR2 (WP11) NARX4 (WP11+WS11) Wind Power Forecasting Models SMOreg MLP Bagging M5R Algorithms 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) P ( t) f ( P( t 1), P( t 2), P( t 3), P( t 4), P( t 5), y( t 1), y( t 2), y( t 4)) -----(5.2) 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.

4 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 125 Actual Wind wind Speed speed Data Wind Speed Prediction Model Predicted wind speed data Wind Turbine Power Curve Model Wind Power Forecast 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 multistep 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.

5 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 126 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. NAR1 (WS11) NARX1 (WS11+WD) NARX2 (WS11,WD, WS10) NARX3 (WS11,WD, WS10, WS09) Wind Speed Forecasting Models SMOreg Bagging M5R M5P Algorithms 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.

6 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 127 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). y ( t) f ( y( t 1), y( t 2), y( t 3), y( t 5)) -----(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) y t) f ( y( t 1), y( t 2), y( t 3), y( t 5), u ( t 1), u ( t 3)) -----(5.4) ( 1 1 where u 1 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

7 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 128 better for larger prediction horizons. The non-linear autoregressive model with wind direction and wind speed of the corresponding period in the previous year (u 2 ) as exogenous variables (NARX 2), after application of feature selection algorithm is defined in Equation (5.5). y t) f ( y( t 1), y( t 2), y( t 3), y( t 5), u ( t 1), u ( t 3), u ( t 4)) -----(5.5) ( The non-linear autoregressive model with wind direction and wind speed of the corresponding period one year before and two years before (u 3 ) as exogenous variables (NARX 3), after application of feature selection algorithm is defined in Equation (5.6) y ( t) f ( y( t 1), y( t 2), y( t 3), y( t 5), u1( t 1), u1( t 3), u2 ( t 4), u3 ( t 1), u3 ( t 7)) -----(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. Parametric Models Four parameter logistic expression Five parameter logistic expression Non-parametric models WTPC Models PSO DE MLP BAGGING M5R M5P Algorithms Fig. 5.4 WTPC Models for Proposed Combined Model

8 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 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 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 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

9 Wind Power (kw) CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 130 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 Wind Speed (m/s) Fig. 5.5 Real-time Data of Sotavento Wind Farm - December 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 MLP BAGGING M5R

10 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 131 Table 5.2 Performance of Proposed Direct Models for Multi-Step Prediction 1 st time step 2 nd time step 3 rd time step 4 th time step 5 th time step 6 th time step NAR2 (SMOreg) NARX4 (SMOreg) MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE 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 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).

11 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 132 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 BAGGING M5R M5P 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.

12 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 133 Table 5.4 Performance of Multi-Step Wind Speed Prediction Models 1 st time step 2 nd time step 3 rd time step 4 th time step 5 th time step 6 th time step NAR1 (M5R) NARX1 (BAG) NARX1 (M5P) NARX2 (BAG) NARX2 (M5P) NARX3 (BAG) NARX3 (M5P) MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE 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 timesteps 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 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

13 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 134 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. 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 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.

14 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 135 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) NARX1 (BAG) NARX1 (M5P) NARX2 (BAG) NARX2 (M5P) NARX3 (BAG) NARX3 (M5P) MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE 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 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

15 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 136 step and Std is a measure of how spread out the values are. The combination of nonlinear 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 1 st time step 2 nd time step 3 rd time step 4 th time step 5 th time step 6 th time step WS model WP model NAR1 (M5R) NAR1 (M5R) NARX1 (M5P) NARX1 (M5P) NARX2 (M5P) NARX2 (M5P) NARX3 (M5P) NARX3 (M5P) DE(4P) DE(5P) DE(4P) PSO(5P) DE(4P) PSO(5P) DE(4P) PSO(5P) MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE

16 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 137 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) NAR1(M5R)+DE(5P) NARX1(M5P)+DE(4P) NARX1(M5P)+PSO(5P) NARX2(M5P)+DE(4P) NARX2(M5P)+PSO(5P) NARX3(M5P)+DE(4P) NARX3(M5P)+PSO(5P) NAR2 +SMOreg NARX4+SMOreg 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

17 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 138 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 databased 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

18 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 139 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 gridconnected 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

19 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 140 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. Wind Speed Prediction Model Predicted Wind Speed Wind Speed Extrapolation Wind Turbine Power Curve Model Wind Resource Estimation Fig. 5.7 Wind Resource Estimation Methodology 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.

20 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 141 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 N and E ( 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 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.

21 Probability CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS Sulur Wind Speed (kmph) 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) N i 1 AEP P( y ) -----(5.7) i 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 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 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.

22 Wind Speed (m/s) Wind Power (kw) CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS Actual Power Estimated Power Wind Speed (m/s) Fig Model Wind Turbine Power Curve 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 and Fig respectively. The annual energy estimated for June 2011 to May 2012 is tabulated in Table z =2m z = 50m z = 80m z = 100m June 2011 July 2011 Aug 2011 Sept 2011 Oct 2011 Nov 2011 Dec 2011 Jan 2012 Feb 2012 Mar 2012 Apr 2012 May 2012 Fig Extrapolated Wind Speed Data of Sulur

23 Energy Estimated (MWh) CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS z = 50m z = 80m z = 100m June 2011 July 2011 Aug 2011 Sept 2011 Oct 2011 Nov 2011 Dec 2011 Jan 2012 Feb 2012 Mar 2012 Apr 2012 May 2012 Fig Estimated Energy Production in Sulur Table 5.8 Annual Energy Estimated in Sulur region Height (m) Annual Energy Estimated (MWh) 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.

24 CHAPTER 5 DEVELOPMENT OF WIND POWER FORECASTING MODELS 145 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.