Intelligent Combined Prediction of Wind Power Based on Numerical Weather Prediction and Fuzzy Clustering

17th IFAC Symposium on System Identification 17th IFAC Symposium on SystemCenter Identification Beijing International Convention Available online at www.sciencedirect.com 17th IFAC Symposium on System Identification Beijing International Center October 19-21, 2015. Convention Beijing, China Beijing International Convention Center October 19-21, 2015. Beijing, China October 19-21, 2015. Beijing, China

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IFAC-PapersOnLine 48-28 (2015) 538–543

Intelligent Combined Prediction of Wind Power Based on Numerical Weather Intelligent Combined Prediction of Wind Power Based on Numerical Weather Intelligent Combined Prediction Wind Power Based on Numerical Weather Predictionofand Fuzzy Clustering Prediction and Fuzzy Clustering Prediction and Fuzzy Clustering Yang Jiaran*. Wang Xingcheng*. Luo Xiaofen** Yang Jiaran*. Wang Xiaofen** JiangXingcheng*. Cheng*** Luo Yang Jiaran*. Wang Luo Xiaofen** JiangXingcheng*. Cheng*** Jiang Cheng*** * Information Science and Technology College,  Dalian Maritime University, Dalian, CO 116026 * Information Science and Technology College, Dalian Maritime University, Dalian, CO 116026 China (e-mail: [email protected]). * Information Science and Technology College, Dalian Maritime University, Dalian, CO 116026 (e-mail: [email protected]). **Huaneng WeihaiChina Power Generation CO., LTD, Weihai, CO 264205 China China (e-mail: [email protected]). **Huaneng *** Weihai Power Generation CO., LTD, Weihai, 264205 China Beijing Electric Power Company, Beijing, CO China **Huaneng *** Weihai Power Generation LTD, Weihai, 264205 China Beijing Electric PowerCO., Company, Beijing, CO China *** Beijing Electric Power Company, Beijing, China Abstract: Wind power prediction accuracy has important implications for the scheduling and stable powersystem. prediction accuracy has important implications for the scheduling stable Abstract: ofWind operation the power An Intelligent Combined Prediction algorithm of Wind Powerand Based on powersystem. prediction accuracy has important implications for the scheduling and stable Abstract: ofWind operation the power An Intelligent Combined Prediction algorithm of Wind Power Based on Numerical Weather Prediction and Fuzzy Clustering is proposed in the paper. Based on numerical operation ofWeather the power system. An Intelligent CombinedisPrediction algorithm of Wind Power Based on Numerical Prediction and Fuzzy Clustering proposed in the paper. Based on numerical weather prediction data and method ofisfuzzy clustering the original NWP Numerical Weather(NWP) Prediction and using Fuzzythe Clustering proposed in thesubtraction, paper. Based on numerical weather prediction (NWP) data and using the methodT-S of fuzzy fuzzy clustering subtraction, the multiple original NWP patterns; data is divided into several typical weather model, time-series model, linear weather prediction (NWP) data andweather using the methodT-S of fuzzy fuzzy model, clustering subtraction, the multiple original linear NWP data is divided into several typical patterns; time-series model, regression model and gray typical model are established respectively according to different whether types; the data is divided into several weather patterns; T-S fuzzy model, time-series model, multiple linear regression model and gray model are established respectively according to algorithms different whether types; the combination of multi-model is optimized using intelligent optimization and the optimal regression model and gray model are established according to algorithms different whether the combination of multi-model isis optimized using respectively intelligent optimization and thetypes; optimal combination prediction model obtained. Prediction results of a domestic wind farm indicate that the combination of multi-model is optimized using intelligent optimization algorithms and the optimal combination predictionprediction model is method obtained. Prediction results of domestic wind power farm indicate thatwith the proposed combination is valid and effective in aashortterm wind prediction, combination predictionprediction model is method obtained. Prediction results of domestic wind power farm indicate thatwith the proposed combination is valid and effective in shortterm wind prediction, better prediction accuracy. proposed combination prediction method is valid and effective in shortterm wind power prediction, with better prediction accuracy. better prediction accuracy. © 2015, IFAC (International Federation ofFuzzy Automatic Control) Combined Hosting by Elsevier Ltd. All rights reserved. Intelligent Optimization; Clustering; Prediction; Numerical Weather Keywords: Intelligent Optimization; Fuzzy Clustering; Combined Prediction; Numerical Weather Keywords: Prediction; Wind Power Intelligent Keywords: Prediction; Wind Power Optimization; Fuzzy Clustering; Combined Prediction; Numerical Weather Prediction; Wind Power   1. INTRODUCTION Weather forecasting method refers to the physical value  1. INTRODUCTION methodprediction refers to (Numerical the physical value With the rapid development of wind power technology, the Weather based on forecasting numerical weather Weather 1. INTRODUCTION Weather forecasting methodprediction refers to (Numerical the physical value With the rapid development of wind power technology, the based on numerical weather Weather number and sizedevelopment of unit capacity of wind turbines and gridPrediction NWP) provided to obtain such direction, wind With the rapid of wind power technology, the based on numerical weather prediction (Numerical Weather number and sizefarms of unit capacity of wind turbines and grid- Prediction NWP) provided to obtain such direction, wind connected wind are expanding; wind power penetration speed, air pressure, air density, weather data, and then fans number and sizefarms of unit of wind turbines and grid- Prediction NWP) provided to obtain such connected wind arecapacity expanding; wind power penetration speed, airthepressure, air information density, weather data,direction, and then wind fans is also growing. Randomness of wind power, intermittent, etc. around physical (including topography, connected wind farms are expanding; wind power penetration speed, air pressure, air density, weather data, and then fans is alsohave growing. Randomness of the windstability, power, intermittent, etc. contours, around thebestphysical information (including topography, will a major impact on power quality, estimate of the surface roughness, around is also growing. Randomness of wind power, intermittent, etc. around the physical information (including topography, will have a and major impact on the stability, quality, best of estimate the surface roughness, around distribution trend of scheduling grid. Topower this end, the contours, obstacles, etc.) the windof hub height wind speed and will have a and major impact on the stability, power quality, contours, best of estimate ofturbine the surface roughness, around distribution trend of scheduling grid. To this end, the obstacles, etc.) the wind turbine hub height wind speed and National Energy Board to develop the relevant rules, clearly direction information, and finally the use of the power curve distribution and trend of scheduling grid. To this end, the obstacles, etc.) of the wind turbine the hubuse height wind speed and National Energy Board to develop the relevant rules, clearly direction information, and finally of the power curve requires that eachBoard wind farm must have three days in advance to derive wind power. and finally the use of the power curve National Energy to develop the relevant rules, clearly direction information, requires thatwind each wind must have three days advance to derive wind power. to predict farm farm generating capacity of in short-term requires thatwind each wind farm must have three days in advance to derive wind power. to predict farm generating capacity of short-term forecasting ability,farm power dispatching agencies will also Use a combination of both numerical weather prediction to predict wind generating capacity of short-term Use a as combination numerical weather input data, of andboth the use of historical dataprediction analysis, forecasting power dispatching agencies forecast will also examine the ability, daily prediction accuracy. Accurately of model Use a as combination of both numerical weather prediction forecasting ability, power dispatching agencies will also model input data, and the use of historical analysis, modeling, and statistical advantages of both physical type examine the has daily prediction accuracy. for Accurately forecast of model as input data, and the use of historical data wind power important significance the power grid and data analysis, examine the has daily prediction accuracy. for Accurately forecast of wind modeling, and statisticalsystem, advantages of both physical type power forecasting the lowest prediction error is wind power important significance the power grid and wind farm and economic operation. and statisticalsystem, advantages of both physical type wind powersafety has important significance for the power grid and modeling, wind power forecasting the lowest prediction error is the best wind power forecasting system. wind farm safety and economic operation. wind power forecasting system, system. the lowest prediction error is wind farm safety and economic operation. the best wind power forecasting Continuous method is one of the earliest prediction methods, the best wind power forecasting system. 2. BASIC IDEA Continuous method is one of the earliest prediction with the deepening of the research, numerical methods, weather 2. BASICofIDEA Continuous method is one of the earliest prediction methods, NWP according to a atmospheric mathematical with the deepening of the research, numerical weather BASIC IDEA prediction method, statistical method and artificialweather neural NWP according to a2.group with the deepening of the research, numerical group of conditions atmospheric mathematical model, using the current weather as the input data prediction method, statistical method and artificial neural according to a group of conditions atmospheric mathematical network method is applied to the wind power prediction. At NWP prediction method, statistical method and artificial neural model, using the current weather as the input data to predict the future weather conditions. NWP can provide network method is applied to the wind power prediction. At present, wind power forecasting methods can be divided into model, using the current weather conditions as the input data network method is applied to the wind power prediction. At to predict the future weather conditions. NWP can provide grid point under different physicalNWP level,can wind speed, present, wind powerstatistical forecasting methods divided into different physical method, method andcan thebe combination to predict the future weather conditions. provide present, wind power forecasting methods can be divided into different grid point under different physical level, wind direction, temperature, humidity, air pressure and speed, other physical method, statistical method and the combination wind model method. grid point under different physical level, wind speed, physical method, statistical method and the combination different wind direction, temperature, humidity, air NWP pressure and other physical quantities forecast, in general, can provide model method. wind direction, temperature, humidity, air NWP pressure other model method. quantities forecast, in days. general, canand provide Statistical forecasting model by a large number of historical physical effective prediction of 3 ~ 10 Because of the wind physical quantities forecast, in general, NWP can provide Statistical forecasting model the by arelationship large number of historical effective prediction ofto 3meteorological ~ 10 days. Because of the wind data for analysis to establish between history turbine output related factors, such as wind Statistical forecasting model the by arelationship large number of historical effective prediction ofto 3meteorological ~ 10 days. Because of the wind data for analysis to establish between history turbine output related factors, such as wind and forecast data between wind power. speed, wind direction, air pressure, so you need to NWP data data for analysis establish thepower. relationship between history turbine output related to meteorological factors, such as wind and forecast data to between wind speed, wind direction, air pressure, so you need to NWP data for wind power prediction model. and forecast data between wind power. speed, wind direction, air pressure, for wind power prediction model. so you need to NWP data for wind power prediction model. This This work work was was financially financially supported supported by by National National Science Science Foundation Foundation of of China China (60574018). (60574018). This work was financially supported by National Science Foundation of China (60574018). This work was©financially supported by National Science Foundation of of China (60574018). 2405-8963 2015, IFAC (International Federation Automatic Control)

Hosting by Elsevier Ltd. All rights reserved. Copyright IFAC responsibility 2015 538Control. Peer review©under of International Federation of Automatic Copyright © IFAC 2015 538 10.1016/j.ifacol.2015.12.184 Copyright © IFAC 2015 538

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The basic idea of this paper is as follows: (1) historical NWP data, using the method of fuzzy subtractive clustering of the original NWP data into several typical weather patterns; (2) based on the Takagi-Sugeno fuzzy neural networks, time series models, multivariate linear regression model, gray model separately for each type of typical weather modeling; (3) the future of NWP data, the first to apply the same principles to determine the weather patterns cluster to which it belongs, and then use each single type corresponding to the weather wind power forecasting models to predict; (4) each single model predicted results as the original data, using the ELMAN network optimum combination of multiple model, get the optimal combination forecast model which should be suitable to the wind farm. Flow chart is as follows:

539

After the pattern vector construction is completed, press the mode structure is formed according to the wind farm cluster sample set NWP historical data. Physical meaning of each pattern vector elements of different dimension, it will affect the results of cluster analysis of patterns normalized vector elements, which can effectively reduce this effect. Assuming the sample set X by n samples, each sample x i (i=1,2, …n) has s mode vector elements, i.e. x i =[x i1 ,x i2 , …x is ], using the equation for sample normalization.  xij'

xij  m j  2, N j 1, 2,s   i 1, M j  mj

(1)

Where: m j is the minimum value of j-th mode vector elements of all samples;

NWP Data

Where: M j is the maximum value of j-th mode vector elements of all samples;

Pattern Structure Fuzzy Clustering

After the sample set normalized denoted as:

Typical weather type 1

Typical weather type 2

……

T-S model

Multiple linear regression

Time Series Model

Typical weather type n

 x11'  x1' p    X      xn' 1  xn' p    '

Grey Model

(2)

Where: model number of elements in the vector p=42; n is the number of samples, the number of days that is involved in clustering analysis.

Based on the ELMAN network combination model Wind power prediction

4. FUZZY CLUSTERING Clustering analysis is the basis of many classification and system modelling for the given data. The purpose of clustering is to extract the fixed features from a large number of data, and thus obtain the concise expression of the system behaviour. The subtractive clustering of the GK clustering algorithm is initialized; clustering validity function is used to determine the reasonable number of clusters, to achieve the effect of automatic classification, can give more reasonable cluster membership results. For this paper, the automatic optimal classification of weather patterns is carried out. The detailed contents of GK fuzzy clustering and subtraction clustering algorithm are please refer to the relevant literature.

Fig. 1. The flow chart of wind power prediction 3. DATA PREPARATION 3.1 Pattern Structure Pattern recognition technology is a reflection of the amount of things characteristic of the sample collection techniques are classified by. Model construction process should follow two principles: First, to minimize the feature vectors in ensuring the correct sample characteristics reflect the premise dimension; the second is to ensure that during the interference pattern recognition feature vectors between the smaller. Geomorphologic information of wind farms generally do not change can not be considered the main mode vectors extracted from the meteorological factors. Accordingly, in weather typing process, a daily NWP series data (time data resolution 15min) based extraction Beijing time 00: 00,04: 00,8: 00,12: 00, 16:00, 20:00 and 24:00 meteorological information to construct pattern vectors. In the variable selection, this paper choose near wind turbines hub height wind speed V, wind direction sine D sine , wind direction cosine D cos , air temperature T, barometric pressure P, humidity H, as the basic variable pattern vector can be expressed as M=[V 0 …V 24 D sine0 …D sine24 D cos0 …D cos24 T 0 … T 24 P 0 …P 24 H 0 …H 24 ].

4.1 Clustering Validity Function Clustering is reasonable number of categories were determined by cluster validity function. This paper takes the form of a simple, low computation based on possibility distribution of cluster validity function. For a given number of cluster centers and membership matrix, the membership factor is defined as: F U ; c  

1 n c 2   jk n k 1 j 1

(3)

The possibility of membership factor is defined as:

3.2 Sample Normalization

P U ; c  

539

1 c  n 2  jk    cj 1  k 1

n



k 1 

jk

  

(4)

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Let Ω c for all the membership matrix U "optimal" finite set, then for a given number of cluster centers c and membership matrix U, clustering validity function based on possibility distribution is as follows: FP U ; c  F U ; c   P U ; c 

5. BASED ON TS FUZZY NEURAL NETWORK MODELING OF TYPICAL WEATHER Based on Takagi Sugeno fuzzy reasoning characteristic, the relevant scholars combined with neural network, it is used to construct neural fuzzy system with adaptive learning ability. Formed by a combination of fuzzy neural network, and at the same time with neural network and fuzzy logic is easy to express human knowledge of the advantages of distributed information storage, and the ability to learn, for complex system modeling and control provides an effective tool. About the T-S model calculating process of MISO fuzzy system in detail, please refer to the relevant literature. Fuzzy neural network structure Takagi-Sugeno model based on the following:

(5)

For U∈Ω c , if present, (U*;c*) satisfy the following formula:





FP U  ; c   min min c FP U ; c  c

(6)

(U*;c*) places as the "best" of valid clusters. 4.2 GK Algorithm Automatically Determine the Clustering Number of Categories GK clustering algorithm automatically determined based on the number of clusters to achieve subtractive clustering process :

1

STEP1: makeδ=0.5, N samples containing sample set X as subtractive clustering, clustering get the upper C max ; STEP2: take c=1,2, …, C max ; Initialization fuzzy membership matrix U ini_c ; By GK clustering algorithm to obtain the ideal membership matrix U c ;

ym

11

1

1

1m

2

2

m

m

y

s

Fig. 2. Takagi-Sugeno model fuzzy neural network structure Typical weather 3.3 was set P i (i=1,2,…c*) in daily NWP data (time resolution 15min) wind speed V, wind direction sine D sine , cosine wind direction D cos , air temperature T, barometric pressure P, humidity H structural model input vector X tj =[V j , D sinej , D cosj , T j , P j , H j ], where t∈P i is the set of elements, j for the first j-th time point t day. With X tj as input data to the corresponding time for the wind power output data TS fuzzy neural network training, get a typical weather model c* Q i (i=1,2,…c*). In this paper, the relevant (ANFIS) initialization and modeling functions MATLAB fuzzy logic toolbox provides adaptive neuro-fuzzy inference system model.

After completing the above steps to get the optimal clustering fuzzy membership matrix U* and cluster number of categories c* (typical weather category number) for the j-th column of the matrix U*, if U* ij (i=1,2,…c* j=1,2,…,n) is the largest, the sample j belong to class i, in accordance with this rule complete weather types, is expressed as a collection of c*,P 1 ,P 2 , …,P c* . According to the following formula to re-calculate the typical weather collection centers: pi

 x x kj

xs

 sm

4.3 Weather Typing

k 1

y2

 1s

STEP3: to satisfy the conditions to judge the validity of the number of categories and clustering fuzzy membership matrix is the optimal effective clustering.

1 pi

y1

x1

1

Find the corresponding cluster validity function U c 、 FP(U;c);

 oij

 l  1,2,  , m   pli   i  1,2, , s 

k

  Pi i 1, 2, , c j 1, 2, , s 

(7)

6. POWER PREDICTION According to numerical weather prediction (NWP) data structure formation and pattern vector pattern vector prediction Day t M t , judging by the Euclidean distance o i (i=1,2,…c*)typical weather pattern vector similarity with M t r it . Typical weather pattern set vector o i =[o i1 ,o i2 , … ,o is ] (i=1,2,…c*), t predict daily pattern vector M t =[m t1 ,m t2 , … m ts ], s for the model number of vector elements. Similarity discriminant formula is as follows:

Where, p i is the i-th class contains a collection of typical weather samples; s is the number of elements of the vector mode. Get the typical weather pattern vector:  o1   o2  O     o    c 

 o11 o  21     oc 1

o12 o22  oc 2



o1s   o2 s       oc s 

(8)

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s

rit  1    oik  mtk 

2

A linear combination of the above prediction, also known as fixed weights of combination forecasting method, has been drawn in the forecast fixed weighting coefficients can not predict problems with environmental change and change, thus affecting the accuracy of the prediction. If the ^ combination of the predicted value Yi satisfies the following formula:

(9)

k 1

Take the highest similarity value Ritmax , represents the prediction date pattern vector M t typical weather patterns o i belonging to the same pattern, namely t day weather conditions typical weather belonging to class i type, class i by Q i (i=1,2, … c*)Typical Weather Forecasting Model t day power. Pattern recognition of the role that judgment based on NWP data to predict the date of the membership type, and then by the predicted date t of NWP model data structure of the input vector X tj =[V j , D sinej , D cosj , T j , P j , H j ] and then by a known model to predict wind power Power tj .

     Yi    Y 1i , Y 2i  , Y mi   

Combination model studied in this paper, including multiple linear regression, time series, gray model and T-S neural network model, specific methods can refer to the relevant literature. Suppose there are m types of predictive models, combination forecasting model expression is as follows:

8. ALGORITHM SIMULATION In this paper, choose some wind farms in Weihai in Shandong province algorithm simulation. Numerical weather prediction (NWP) model can well predict the weather but the existence of a particular wind farms with poor local weather forecasts fact. In order to avoid this situation and make full use of wind turbines more complementary, total output of the smooth features, select the geographical position relatively concentrated, the surrounding physical information (including the surrounding terrain, contour, the surface roughness and obstacles, etc.) are relatively consistent 28 typhoon electrical units as test object (each rated power 1.5 MW, the total power of 42 MW).

(10)



Wherein Yi represents a combination of the predicted value of 

the i-th point; Yji represents the j-th prediction model predicted value of the i-th point;  j represents the weight of the j-th model. Combination forecasting model focuses on solving the weight, there are always errors predicted values and the real value, based on a combination of predicted

With 2012 year observation and corresponding power in the history of the SCADA system of NWP data as the original data, temporal resolution for 15 min, selected from the four quarters from 2013 in November 2, 5, 8, the corresponding data as test data, after eliminating abnormal data, the overall availability of data is 96.8%.



values Yi i-th point and the real value of the deviation between Yi is  i , then, 

m



m

m

 i  Yi  Yi    j Y ji  Yi   j    j  ji j 1

According to section 2.2 form a cluster sample set X´, in accordance with section 3 steps to cluster sample set X´ 2012 get 9 typical weather set P 1 ,P 2 , … ,P 9 and O 1 ,O 2 , … ,O 9 collection center, then use T-S fuzzy neural network, multiple linear regression, time series, the grey model, respectively, to establish the model of typical weather. With the single model to forecast the test sample, will predict the results as the ELMAN network input combination forecast, get the final forecasting result.

(11)

j 1 j 1

Where  ji represents the prediction error of the j-th prediction model of the i-points. Forecast set of n points in the forecast period, the optimal weight should be met: 2 n  m   min F      j  ji   i 1   j1  m   j 1,  j  0   j 1 

(13)

Where  is a nonlinear function, called the combination forecasting nonlinear combination forecasting, namely variable weight combination forecasting. Clearly, variable weight combination forecasting system can reflect the nonlinear, given the right combination forecasting more than reasonable. Combination forecasting using neural networks in recent years nonlinear combination forecasting a major breakthrough. In this paper, ELMAN neural network input m different models for the first time i predicted    value Y 1 i , Y 2 i  , Y m i . Output in the learning stage is the actual value of the time i, in the forecast period as a combination of the predicted value at time i. Through a network of learning and training constantly revised weights, in order to establish a nonlinear combining function between the input and output.

7. COMBINATION PREDICTION MODEL Combination forecasting method is a prediction method proposed by Bates and Granger in 1969, the basic idea is to predict the different models and methods to combine utilization of various prediction methods provide information to the proper way to get the weighted average a combination of predictive models.

m     Y  j Y ji   i j 1  m    j 1,  j  0  j 1

541

Taking the mean absolute percentage error (MPAE) as the error indicator.

(12)

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2015 IFAC SYSID 542 October 19-21, 2015. Beijing, China

EMPAE 

1 N

Yang Jiaran et al. / IFAC-PapersOnLine 48-28 (2015) 538–543

N

PMi  PPi

i 1

PMi



(14)

Where P Mi represents the actual power at time i, P Pi represents i-th time prediction power, N represents the number of all samples. 4 months were randomly selected from a day (February 15, respectively, May 20, August 13 and November 25) for 1 to 24 hours of 96-step prediction, prediction statistics are as follows:

Fig. 4. On November 25, wind power prediction results.

Table 1. The mean absolute percentage error of the prediction methods(MPAE/%)

Date Time Multiple linear Grey T-S neural Combined series regression Model network Model 15/0222.1% 25.9%

19.8% 16.7%

9.7%

20/0526.7% 30.4%

23.6% 15.3%

11.0%

13/0829.6% 33.5%

21.4% 13.2%

10.8%

25/1128.7% 31.7%

20.0% 16.4%

9.5%

Fig. 5. On February 15, wind power prediction results.

Table 1 shows the ways each day of the predicted results mean absolute percentage error (MPAE). Can see from the table data, based on T-S fuzzy neural network prediction results on the whole is better than any other single forecasting method, combination forecast overall improves the prediction precision, shows the superiority and effectiveness of combination forecast model. A neural network nonlinear combination forecasting can be well performance of wind power system nonlinear, and combination function calculation is simple, more applicable to short-term wind power prediction.

Fig. 6. On May 20, wind power prediction results.

Fig. 7. On August 13, wind power prediction results. As can be seen from Figure 4~7, the method has good prediction herein prediction, forecasting wind power curve shape with substantially the same as the actual power curve, a smooth error. Fig. 3. The forecasting model weights trend chart.

After comparison, the best prediction method using a single model is based on the T-S fuzzy neural network model, but based on nonlinear combination forecasting ELMAN neural network to improve the prediction accuracy of the whole, this is because the combination forecasting method to predict utilization of each single information contained method to avoid focus on the impact on one or more factors prediction accuracy.

Figure 3 shows the weights of each single prediction methods of prediction curve. It can be seen that with the increase of time, the weight of time series prediction gradually decreases, and the other two single prediction method of weight also gradually reduced, the weight of T-S fuzzy neural network gradually increase, this suggests that when predicting when time is not long, such as only predict a few steps, time series forecasting have an advantage, and with the increase of forecast time, based on the classification of T-S fuzzy neural network prediction method is superior to other methods.

In combination forecasting method using neural network nonlinear combination forecasting method is better than the linear combination forecasting method, which is a linear combination forecasting method because the right to re-set match to a large extent dependent on the degree of historical samples, and the neural network nonlinear not too much to 542

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pursue a combination forecasting method of fitting accuracy of historical sample, but every time forecasting, wind power by learning nonlinear dynamic characteristics of heavy set right. Thus, the wind power has better randomness large prediction accuracy. 9. CONCLUSIONS Although the wind has strong randomness, bring difficulty to the wind power prediction, but as long as reasonable use of weather phenomenon itself has the characteristics, still can make the wind power prediction to achieve high precision. This paper puts forward a kind of based on fuzzy clustering, weather, classification of combination forecast model, prediction results show that this prediction method is feasible and effective. The wind farm access to the power system operation dispatch has important practical value. The future study of wind power and wind speed prediction, mainly from the use of new prediction model, the analysis of wind power statistical relationship with meteorological factors, such as the characteristics of wind power into consideration. REFERENCES Bates J M, Granger C W J. (1969).The combination of forecasts. Operational Research Quarterly, 20(4), 451468. Cai Wei, Cheng Junjie. (2011). Research on GK Fuzzy Clustering Algorithm Based on Subtractive Clustering. Journal of Lanzhou Jiaotong University, 30(6), 50-55. Cai Kai ， Tan Lunnong, et al. (2008). Short-Term Wind Speed Forecasting Combing Time Series and Neural Network Method. Power System Technology, 32(8), 8285. Chi Yongning，Liu Yanhua，et al. (2007).Study on impact of wind power integration on power system ． Power System Technology，31(3), 77-81. Dong Lei Wang Lijie, et al. (2008). Modeling and Analysis of Prediction of Wind Power Generation in the Large Wind Farm Based on Chaotic Time Series. Transactions of China Electrotechnical Society, 23(12), 125-129. Deng Huiwen. (2006). Based on subtractive clustering and clustering validity evaluation of FCM clustering. Journal of chongqing institute of technology, 20 (5), 59-62. Fan Jiulun, Pei Jihong. (1998). Cluster Validity Based on Possibilistic Distribution. Acta Electronica Sinica, 26(4), 113-115. Giebel G ， Landberg G, et al. (2003).State-of-the-art on methods and software tools for short-term ， prediction of wind energy production. European Wind Energy Conference & Exhibition, 40-47. Madrid，Spain． He Jing, Wei Gang, et al. (2003). Fuzzy improvement of linearregression analysis for load forecasting ． East China Electric Power, (11), 21-23． Kyung Bin，Young Sik Baek, et al. (2005). Short-term load forecasting for the holidays using fuzzy linear regression method．IEEE Trans on Power Systems, 20(1), 96-101. LI Hongtao, Ma Zhiyong, et al. (2012).Forecasting system based on numerical weather prediction. China Power, 45(2), 64-68.

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