WIND POWER IS A mature renewable energy technology

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1 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 1, JANUARY Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features Jie Shi, Zhaohao Ding, Student Member, IEEE, Wei-Jen Lee, Fellow, IEEE, Yongping Yang, Yongqian Liu, and Mingming Zhang Abstract Very-short term wind power forecasting is one of the most effective ways to deal with the challenges of increased penetration of wind power into the electric grid due to its fluctuation and volatility. To improve wind power forecasting by taking advantage of each independent forecasting model, a hybrid model is proposed by means of grey relational analysis and wind speed distribution features. The weight of each independent model is estimated according to different wind speed subsection and similar wind speed frequency. The case study shows that the hybrid forecasting model has broader applications in very-short term (15-minute-ahead) wind power output forecasting. Index Terms Grey relational analysis, hybrid model, very-short term wind power forecasting, wind speed distribution features. I. INTRODUCTION WIND POWER IS A mature renewable energy technology for electricity generation with high efficiency and minimal pollution and greenhouse gas emissions [1]. However, the increasing wind power penetration level will affect the operation and reliability of the grid due to the intermittent nature of wind generation. A reliable and accurate wind power forecasting is one of the most effective and economically feasible solutions to this problem. The Electric Reliability Council of Texas (ERCOT), which has the highest wind generation capacity among major independent system operators (ISO) in the United States, is a good example of integrating wind generation resources (WGR) into the bulk power system. In 2011, ERCOT s wind generation capacity accounted for 9452 MW of its MW total installed generation capacity, and is projected to increase to MW by 2014 [2]. The rapidly increasing penetration of WGRs makes wind power forecasting Manuscript received February 15, 2013; revised June 13, 2013, August 27, 2013, and September 19, 2013; accepted September 19, Date of publication December 12, 2013; date of current version December 24, Paper no. TSG J. Shi is with Beijing Information Science and Technology University, Beijing, , China ( shijie0921@gmail.com). Z. Ding and W.-J. Lee are with the Energy Systems Research Center, University of Texas at Arlington, Arlington, TX, USA ( zhaohao. ding@mavs.uta.edu; wlee@uta.edu). Y. Yang and Y. Liu are with State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing, , China ( yyp@ncepu.edu.cn; yqliu@ncepu. edu.cn). M. Zhang is with Electric Power Research Institute, China Southern Power Grid, Guangzhou, , China ( zhangmm@csg.cn) Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TSG a critical factor for ERCOT s real-time operation. In ERCOT s nodal market, qualified scheduling entities (QSE) with the WGRs in their portfolios would submit a current operating plan (COP) based on the wind power forecasting results. ERCOT would run a reliability unit commitment (RUC) and a real-time security constrained economic dispatch (SCED) based on the COP for the WGR in the scheduling. Moreover, wind power forecasting is being utilized to determine system reserves [3]. Therefore, a more accurate very-short term wind power forecasting could help ERCOT and other ISOs effectively enhance their system reliability. With an improved wind power forecasting technique, ISOs and other market participants could better anticipate the operating conditions for WGRs in both the day-ahead market and the real-time market to maintain system frequency in a cost-effective way [4]. As an area with the fastest development of WGRs [5], China also demonstrates how important wind power forecasting can be for generation scheduling and real-time operation. WGRs need to provide both a day-ahead (the next day from 0:00 to 24:00) and a real-time (next 15 minutes to next 4 hours) wind power forecasting report to the Grid Dispatch Center. System operators schedule wind generation based on the forecasting results and system conditions [6]. In normal operation, the WGR has a higher priority than traditional fossil units [7]. The mismatch between the forecasting result and actual wind power output can negatively impact system reliability. A penalty would be applied to the WGR if the wind power forecasting results cannot satisfy the accuracy requirement [6]. Therefore, an improved wind power forecasting algorithm would help both WGRs and system operators. Wind generation is chaotic and stochastic by nature, making wind power forecasting highly challenging, particularly for short time frames. Forecasting methods include the numeric weather prediction (NWP) method, the statistic method, and the intelligent method. Among them, the NWP method has more forecasting precision over a long time frame but requires more physical information [8]; the statistic method and the intelligent method which rely on current local observations are suitable for short-term wind power output forecasting and employ the persistence method, multiple linear regression (MLR), and an auto-regressive and moving average (ARMA) model [9], [10]. Currently, intelligent methods based on artificial neural networks (ANN) or support vector machines (SVM) have gained more attention from researchers due to the applications of artificial intelligence in power systems. Many improvements in forecasting precision for short-term time frames can be realized by utilizing intelligent approaches [11], [12] IEEE

2 522 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 1, JANUARY 2014 Though some improvements for forecasting precision have been achieved by including certain algorithms [13] [15], there are still problems with forecasting algorithms using separate models. No single approach can gain widespread industry acceptance since every model has its advantages and disadvantages [16]. When these models are applied to different wind farms, their forecasting accuracy varies due to the distinct characteristics of the data. To overcome this problem, this paper presents a complexvalued forecasting model based on grey relational analysis and wind speed distribution features. The principle of grey relational analysis is introduced in Section I. The wind speed distribution feature analysis based on a wind farm in China is implemented in Section II. Based on their forecasting accuracy at different wind speeds, a very short term dynamic hybrid wind power forecasting model that combines the least square support vector machine (LSSVM) and the radial basis function neural network (RBFNN) is proposed in Section III, followed by case studies. The conclusions are shown last. II. HYBRID FORECASTING MODEL BASED ON GREY RELATIONAL ANALYSIS A. Grey Relational Analysis The degree of correlation is determined by analyzing the correlation between the object series and the reference series in addition to the developing promotion among every factor according to the principle of grey correlation [17]. There are more differentials between the two series. The more the differentials are, the more the correlation they have. The less two series differ, the more correlation they have. The reference data series is shown as, and the object data series are, where are the values of and in the moment, r. After the non-dimensional transformation, the correlation coefficient between two series at moment is shown as follows: (1) where is the correlation coefficient between two series at moment; is the minimization of differentials between the reference series and object series, is the maximization of the differentials; is the distinguishing coefficient. The correlation between two series is the average value of all the correlation coefficients, and is given as follows: B. Hybrid Forecasting Model A hybrid forecasting model is proposed in this paper. The establishment and solution steps of the forecasting combination approach are showed as follows: 1) Utilize Two Independent Models to Forecast Very-Short Term Wind Power Output: The forecasting approach LSSVM (2) (method 1) and RBFNN (method 2) are selected to forecast the 15-minute-ahead wind power output separately with the same dataset. They have the same input data, and this dataset contains measurements for the previous 15, 30, and 45 minutes of wind speed, wind power output, cosine and sine of wind direction. The wind power output historical data from 15 minutes after the present time are selected as the output [18]. The model output is historical wind power output. The layout of RBFNN is frame. All the data have normalization and anti-normalization solutions. The forecasting result sequences and could be obtained after model training. Meanwhile, it takes as the testing data. The historical data in the same time frame is used to calculate the correlation between the above two forecasting sequences. 2) Data Preprocessing: The dimensionless methods used to transfer the sequence into independent scalars include the extreme difference method, equalization, standardization and initialization, and so on. Equalization is superior for analyzing the correlation between two progressions [19]. In this paper, the equalization method is selected to change the three series into scalars according to the uniform standard. The formula is presented as follows: where is the value on moment for every sequence; is the scale of every sequence; and is the sequence component after equalization. 3) Correlation Calculation: After data preprocessing the forecasting result sequences and using method 1 and method 2 are transferred to and respectively. Then the multistage model can be shown as follows: where is the forecasting value on moment; is the correlation between and ; is the correlation between and ;and,. The correlation matrix can be obtained:. (3) (4). (5) 4) Hybrid Model Forecasting: The back propagation neural network (BPNN) forecasting model was chosen to obtain the correlation matrix in the next 15 minutes. With the forecasting results sequence which used method 1 and method 2 as the input, W can be calculated in step (4) as the object. Through network training, can be obtained. The weight matrix is after normalization, and the final forecasting result can be presented as follows: III. WIND SPEED DISTRIBUTION FEATURES The power output of WGRs is significantly affected by wind speed, which is one of the most important parameters for analyzing and evaluating the quality of wind power resources. Con- (6)

3 SHI et al.: HYBRID FORECASTING MODEL FOR VERY-SHORT TERM WIND POWER FORECASTING 523 TABLE I THE PARAMETERS OF WEIBULL DISTRIBUTION IN EVERY MONTH IN 2010 (K: SHAPE PARAMETER, C: SCALE PARAMETER) Fig. 1. The Weibull distribution at the height of 70 m in 2006 and sequently, getting a better understanding of wind speed patterns would benefit the performance prediction of WGRs, the power system unit commitment, and the economic dispatch. It is important to study the wind speed distribution patterns to obtain comprehensive wind speed statistic characteristics. The Weibull distribution is generally recognized as a suitable probability density function for analyzing wind speed distribution patterns. The Weibull distribution is a one-peak-two-parameter function which is shown in (7). where k determines the shape of the probability distribution plot and c determines the scale of wind speed distribution. By applying Weibull fitting to the 2006 and 2010 wind speed distributions of an example wind farm, the distribution curve can be created, as shown in Fig. 1. The wind speed distribution features of these two years are very similar, which proves that the wind speed distribution pattern does not change much on a yearly basis. This steady wind speed distribution characteristic makes annual or longer-term wind power forecasting for this wind farm feasible. To analyze the wind speed distribution for different months, Weibull fitting is also applied to the data from the example wind farm on a monthly basis. The values of shape parameter and scale parameter of the fitting curving are shown in Table I. In a Weibull distribution, the shape parameter k has a significant effect on the shape of distribution curve. If, the probability distribution function would be monotonic with a mode of 0 and distribution density of x. If, the probability distribution would be an exponential distribution. If,the probability distribution would follow a Rayleigh distribution. If, the probability distribution would be similar to a normal distribution. According to Table I, the wind speed distribution shows some similar but not identical behavior patterns. Therefore, it is feasible to consider wind speed distribution on a monthly basis to conduct wind forecasting. (7) IV. VERY-SHORT TERM WIND POWER HYBRID FORECASTING MODEL BASED ON GREY RELATIONAL ANALYSIS The purpose of wind power forecasting is to achieve a relational analysis to create a model for WGR data on a monthly basis by using the wind speed distribution pattern. In this way, it is possible to establish the independent monthly model according to the features of each month. The forecasting accuracy of the grey relational analysis hybrid model is significantly improved compared to the independent model. A weight matrix is trained based on different wind data and the operating conditions of WGR. In this training process, plenty of relational data is required. Therefore the typical way to obtain a relational matrix is by using the forecasting results of the relational matrix during the training range. Thus two relationships can be achieved between the independent model and the measured value. However, this method is limited by its longer time consumption and the need for a larger sample size. If the weight values could be defined before the training process, the forecasting results can be directly obtained through the weight matrix. In this way, the training time for weight matrix can be reduced, which makes practical application more convenient. Applying this idea, the authors proposed a short-term wind power hybrid forecasting model based on wind speed distribution patterns and grey relational analysis. The detailed modeling process is shown as follows: 1) Calculate the grey relation between the forecasting value of the independent forecasting model and the measured value. Determine the weight matrix which reflects the changes of forecasting value and measured value along with time. 2) Combine the wind speed sequence, which is changing along with the weight matrix, and form wind-speedweight matrix S as a preparation for further segmentation of wind speed and weight. 3) Divide S into different sections according to wind speed distribution frequency on a monthly basis. Because the example wind farm is located on flat terrain, the influence of wind direction is ignored, and only wind speed distribution features are considered in this paper. Table II shows the example wind farm s wind speed sections and corresponding frequency for every month of According to Table II, the wind speed of each month is segmented into different sections with an ascending order based on the similarity of frequency. It can be observed that there is a seasonal pattern of wind speed distribution, i.e., the corresponding frequency of high wind speed section is higher in winter and spring (from November to next May), which means the wind speed is relatively high in those two seasons. 4) Determine the weights and as the mean weights between the two independent forecasting models and the measurement values, which should satisfy,

4 524 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 1, JANUARY 2014 TABLE II THE WIND SPEED SECTIONS AND FREQUENCY IN EVERY MONTH TABLE III THE WEIGHT DATABASE OF HYBRID FORECASTING MODEL IN EVERY MONTH IN WIND FARM,and before building up the weight database. 5) The corresponding weights and can be read in the database according to the wind speed value which is obtained via numerical weather prediction. Utilize and to do weighted summation with independent forecasting models to calculate the forecasting power value of the next moment. V. CASE STUDIES With the wind farm which is mentioned in Section II as the case studies, and the historical data range from 01/01/2010 to 12/31/2010, the weight database is established according to the principle of keeping wind speed frequency similar in every segment. The weights are trained separately in every wind speed segment, and all the values are shown in Table III. After obtaining the weight database, the forecasting processes are implemented to get the final output. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are introduced here to evaluate the performance of the hybrid forecasting model based on the measurement data of 2010, and the 15-minute-ahead power output value is forecasted. The historical data is 15, 30, and 45 minutes ahead of the forecasting time moment for each 15-min-ahead forecast. Because of missing data from SCADA, the data from January, September, October, and November are ignored. The MAPEs Fig. 2. The MAPE comparison of combination model, LSSVM model and RBFNN model. of forecasting models including LSSVM, RBFNN, and hybrid model are demonstrated in Fig. 2 with the average of two independent models as a comparison, and the RMSEs of the forecasting models can be obtained as well. (8) (9)

5 SHI et al.: HYBRID FORECASTING MODEL FOR VERY-SHORT TERM WIND POWER FORECASTING 525 Fig. 3. The forecasting results of hybrid model, LSSVM model, and RBFNN model. where: is forecasting value; is true value (historical data); is the capability of wind farm which is the rated wind power summation of each wind turbine (183 MW in this case); and is the sample scope. From Fig. 2, it can be seen that the forecasting accuracy of the hybrid model is better than both of the two independent models as well as the average of the errors from the two models. The RMSEs support the results shown in Fig. 2. Fig. 3 is the comparison of forecasting results between the hybrid model, the individual models, and the average of the two models in certain contentious test moments of April, with the comparison of the actual data. Fig. 3 shows good performance of the proposed hybrid model for forecasting 15-min-ahead wind power, and the forecasting accuracy is better when the wind power output is low and fluctuating. When the wind power output of the wind farm is lower than 10 MW, the LSSVM model and the RBFNN model show large forecasting errors while the hybrid model fits well with the actual data. When wind power output is fluctuating, the hybrid model is steadier than the individual models. Hence the overall forecasting accuracy is improved in the hybrid model compared to the two individual models. VI. CONCLUSIONS There are various algorithms and models which are applied in wind power output forecasting. Every method has its own advantages and shortcomings in accordance with different conditions like terrain, wind conditions, and the features of operation data. Until now, there no model could guarantee reliable performance in every condition and in every wind farm. Therefore, a hybrid model which combines the independent forecasting models has the potential to improve wind power output forecasting accuracy. In this approach, the advantages of every independent model are preserved by optimizing the weights of each model. In this paper, a hybrid forecasting model with the combination of LSSVM model and RBFNN model is proposed based ongreyrelationalanalysisand wind speed distribution features. The weight database is established according to different monthly wind speed segments. With this database, the forecasting process becomes simple. With the forecasting value of wind speed (obtained by numerical weather prediction) in every month, the weights of the two independent models can be extracted from the database. This approach can not only improve forecasting accuracy but also reduce computational burdens. From the results of the case study, it is shown that the MAPE and RMSE from the hybrid model are 2.37% and 3.79%, which are better than those in LSSVM and RBFNN. REFERENCES [1] U.S. Environmental Protection Agency, Inventory of US greenhouse gas emissions and sinks: , [2] H. Hui, C. N. Yu, R. Surendran, F. Gao, and S. Moorty, Wind generation scheduling and coordination in ERCOT Nodal market, in Proc. IEEE Power Energy Soc. Gen. Meet., 2012, pp [3] D. Maggio, C. D Annunzio, H. Shun-Hsien, and C. Thompson, Utilization of forecasts for wind-powered generation resources in ERCOT operations, in Proc. IEEE Power Energy Soc. Gen. Meet., 2010, pp [4] H.Shun-Hsien,D.Maggio,K.McIntyre,V.Betanabhatla,J.Dumas, and J. Adams, Impact of wind generation on system operations in the deregulated environment: ERCOT experience, in Proc. IEEE Power Energy Soc. Gen. Meet. (PES 09), 2009, pp [5] D. Yu, C. Yonghua, L. Xueming, and Q. 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Eng., 2008, vol. 1, pp Jie Shi received the B.S. degree in building environment and equipment engineering, from Shandong Jianzhu University, Jinan, China, in 2007 and the M.S. and Ph.D. degrees in thermal engineering from North China Electric Power University, Beijing, China, in 2009 and 2013, respectively,. She is currently on the faculty at Beijing InformationScienceandTechnologyUniversity,andshe was a visiting student in the University of Texas, Arlington, TX, USA, from 2010 to Her research interests include wind power output prediction, applications of artificial neural networks and support vector machines to wind power output. Zhaohao Ding (S 11) received his B.S. degree from Shandong University, Jinan, China, in He is currently pursuing his Ph.D. degree at the University of Texas at Arlington, TX, USA (UTA). He is also a Research Member of the Energy Systems Research Center (ESRC). His research interests include renewable energy integration, power system planning and operation, microgrid operation and control, and power markets. Wei-Jen Lee (S 85 M 85 SM 97 F 07) received the B.S. and M.S. degrees from National Taiwan University, Taipei, Taiwan, and the Ph.D. degree from the University of Texas at Arlington, TX, USA, in 1978, 1980 and 1985, respectively, all in electrical engineering. In 1985, he joined the University of Texas at Arlington, where he is currently a Professor in the Electrical Engineering Department and the Director of the Energy Systems Research Center. He has been involved in the revision of IEEE Std. 141,339,551, and 739. He is the Secretary of the IEEE/IAS Industrial and Commercial Power Systems Department (ICPSD) and an Associate EditoroftheIEEEIndustryApplication Society and the International Journal of Power and Energy Systems. He is the Project Manager of the IEEE/NFPA Collaboration on Arc Flash Phenomena Research Project. He has been involved in research on utility deregulation, renewable energy, smart grid, micro-grid, arc flash, load forecasting, power quality, distribution automation and demand-side management, power system analysis, online real-time equipment diagnostic and prognostic systems, and microcomputer-based instruments for power systems monitoring, measurement, control, and protection. He has served as the Primary Investigator (PI) or Co-PI of over 90 funded research projects. He has published more than 200 journal and conference proceeding papers. He has provided on-site training courses for power engineers in Panama, China, Taiwan, Korea, Saudi Arabia, Thailand, and Singapore. He has refereed numerous technical papers for IEEE, IET, and other professional organizations. Prof. Lee is a Registered Professional Engineer in the State of Texas. Yongping Yang received the B.S. degree in solid rocket motor engineering from Beijing Institute of Technology, Beijing, China, in 1989, and the M.S. degree in thermal engineering from North China Electric Power University, Beijing, in 1992, and the Ph.D. degree in engineering thermophysics from Chinese Academy of Sciences, Beijing, in He is currently a Professor in the School of Energy, Power and Mechanical Engineering in North China Electric Power University. He has been involved in research on thermodynamic analysis and system integration of energy system, theory method on energy conservation of coal-fired generating units, thermal application on solar energy, and so on. Yongqian Liu received the B.S. degree in hydroelectric power engineering from North China Institute of Water Conservancy and Hydroelectric Power, Wuhan, China, in 1986, the M.S. degree in hydroelectric power engineering from North China Institute of Water Conservancy and Hydroelectric Power, Wuhan, in 1992, and the Ph.D degree in Production Automation (France)/Ph.D in Hydroelectric Power Engineering (China), under a joint doctoral program between Huazhong University of Science & Technology, Wuhan, and University of Henri Poincare Nancy 1, Nancy, France, in He is currently an Associate Professor in the School of Renewable Energy, North China Electric Power University, Beijing. He has been involved in research on wind power prediction, wind turbine generation condition monitoring, wind power plant design. Mingming Zhang Ph.D., is Senior Engineer and person in charge of Smart Electricity Consumption & Metering Laboratory in Electric Power Research Institute, China Southern Power Grid, Guangzhou, mainly engaged in demand-side management, electric vehicles, marketing technology, metering device technology research, etc.