Support Vector Machine Technique for Wind Speed Prediction

Transcription

1 Internatona Proceedngs of Chemca, Boogca and Envronmenta Engneerng, Vo. 93 (016) DOI: /IPCBEE V93. Support Vector Machne Technque for Wnd Speed Predcton Yusuf S. Turkan 1 and Hacer Yumurtacı Aydoğmuş 1 Istanbu Unversty Aanya Aaaddn Keykubat Unversty Abstract. After pubshng the frst renewabe energy aw of Turkey whch was enacted n 005, many enterprsers started to make nvestments on renewabe energy systems. Wth government encouragement to utse wnd technooges, producton of eectrcty va wnd farms became an attractve nvestment aternatve for many nvestors. The wnd speed s one of the most mportant parameter n determnaton of the wnd energy potenta of a regon. For ths reason, n a potenta regon, wnd speed data are measured houry and saved for one year and these data are used n measurement of the wnd potenta of that regon. The success of the technques predctng the wnd speeds s fary mportant n fast and reabe decson-makng for nvestment on wnd farms. In the present study, the annua wnd speed vaues of observed regon n Turkey s anayzed. Support Vector Machne (SVM) technque s used for the predcton of wnd speed vaues at dfferent attudes. The resuts of the anayss and those obtaned from Artfca Neura Networks (ANN), whch s the most wdey used method n ths fed, were compared wth each other. The resuts show that SVM s a practcabe technque n the predcton of the wnd speed for nvestment on wnd farms. Keywords: support vector machnes, artfca neura networks wnd energy, renewabe energy nvestments. 1. Introducton Over the ast decades, Turkey s economy has consderaby deveoped and ts producton voume has ncrementay grown. Turkey has become one of the fastest growng energy markets n the word. As energy demand ncrease, Turksh government has started to promote new energy nvestments and made some reguatons on renewabe energy sources to ncrease the share of renewabe sources n the country s tota nstaed power. Smar to other countres, Turkey s aso makng progress n the use of renewabe energes. Wthn ths scope, accordng to the Strategc Pan of the Mnstry of Energy and Natura Sources, t s ntended to ncrease the overa eectrcty from renewabe sources. Wnd energy one of the most envronmentay frendy source s an mportant renewabe energy source whch has great potenta to essen Turksh dependence on tradtona energy resources ke gas and coa [1]. In the Strategc Pan of the Mnstry of Energy and Natura Sources, one of the target s to ncrease the produce of the eectrcty from renewabe sources and aso under renewabe sources t s amed to ncrease the estabshed wnd power capacty from 759MW n 014 to 10000MW n 019 []. In order to acheve these targets, the government ntroduced new stmuus packages and provded some convenence for renewabe energy nvestments. Ths s qute encouragng for enterprsers to make nvestments on ths fed. Resource-based eectrcty producton n Turkey s shown n Fg. 1. Predctng what w happen n the future usng the avaabe data has aways been of nterest for nvestors and deveopers. The wnd speed vaues has a cruca mportance for the wnd farm nvestment decson probem. The wnd speed s one of the most mportant parameter n determnaton of the wnd energy potenta of a regon. For ths reason, n a potenta regon, wnd speed data are measured houry and saved for one year and these data are used n measurement of the wnd potenta of that regon. For ths purpose, the measurement staton s paced at a pont of the regon whch s representatve to that fed. In the farm fed, the heght of the measurement staton, whch s ocated perpendcuar to the drecton of the 145

2 domnatng wnd, s commony two-thrd of the heght of the wnd turbne. The measurements coud be performed at dfferent attudes, e.g. 10m, 30m and 50m. These measurements are necessary to make a decson for nvestment. However, as they are ong-term and expensve, they brng about extra cost and aso proonged the duraton to the nvestment. For ths reason, the success of the wnd speed predcton methods for dfferent attudes coud offer fast, reabe and cost-effectve way by whch the nvestment coud be panned we-n advanced. Fg. 1: Dstrbuton of produced eectrcty from dfferent energy sources n Turkey []. In Turkey, the producton of eectrcty through wnd energy connected to the grd started n 1998 and ncreased one fod n each year after 005. As seen n Fg., wnd power ndustry and the constructon of wnd farms underwent rapd deveopment, whch further acceerated technoogy deveopment, n 010 [3]. (a) (b) Fg. : (a) Tota nstaed capacty of wnd power n Turkey [3] (b) Instaed wnd power capacty of the word [4]. Wnd energy ndustry depends on wnd speed forecasts to hep determne facty ocaton, facty ayout, as we as the optma use of turbnes n day to-day operatons. There are physca, statstca, artfca neura and hybrd methods on the predcton of wnd speed. Especay, n recent years, artfca ntegence technques, ke artfca neura networks (ANN), fuzzy ogc and support vector machnes (SVM), and hybrds of these methods are wdey used n the predcton of the wnd speeds. In a revew study, presentng the prevous studes on the predcton of the wnd speed and the energy produced, Le et a. state that artfca technques are more successfu than the tradtona technques and hybrd modes, whch come out nowadays, of cause are advanced ones and have ess error than others [5]. 146

3 . Support Vector Machnes The foundatons of support vector machnes (SVMs) have been deveoped by Vapnk [6] and have been ncreasngy used n dfferent forecastng probems. Successfu forecastng studes were performed wth support vector regresson (SVMr) n dfferent feds such as producton forecastng [7], speed of traffc fow forecastng [8] and fnanca tme seres forecastng [9]-[11]. Aso SVMr s used as a predctor to determne wnd speed [1], [13]. SVMr formuaton s gven beow; The smpest cassfcaton probem s two-cass near separabe case. Assume that there s a tranng set whch has number ponts. d ( x1, y1),...,( xn, y n), x R y, 1, 1 (1) Suppose that there are some hyper panes that separates two casses can shown as w. x b () where w s weght vector whch s norma to hyperpane, and b s the threshod vaue. In the smpest neary separabe case, we seek for argest margn. Margn borders can be formuated as w. x b 1 y 1, w. x b 1 y 1 (3) Eq. (3) can be generazabe as y ( w. x b) 1, 1,..., The dstance between margn borders s d (5) w Here w s the Eucdean norm of w. Accordng to theory, to determne unque souton wth fndng optma hyperpane d must be maxmzed. To cacuate optma hyperpane we have to mnmze 1/ w (6) subject to eq. (3). Ths quadratc optmzaton probem can be soved wth Lagrange Mutpers. 1 L( w, b, ) w [ y( w. x b) 1] 1 Eq. (7) s a Lagrangan where w and b are prma varabes and α s dua varabe. To fnd the optma souton of the prma optmzaton probem (Eq. 7) we have to mnmze prma varabes w and b. L( w, b, ) w (8) L( w, b, ) b (9) After cacuatng above dfferenta operatons, eqs. (10) and (11) are found. y, 1,..., 1 w yx, 1,..., 1 By usng a generazed method of Lagrange mutpers caed Karush Kuhn Tucker condtons we can provde beow equaton where α 0 ponts from the eq. (4). Those ponts are subset of tranng data wth the non-zero Lagrangan mutpers caed Support Vectors. [ y ( w. x b) 1], 1,..., 147 (4) (7) (10) (11) (1)

4 We can transform eq. (1) nto equaton (13) subject to eqs. (10,11). In our Lagrangan equaton, there are ony dua varabes after substtuton prma varabes w and b. Now, our probem s a dua optmzaton probem, t can be soved as shown beow, Maxmze subject to eq. (10). 3. Data Sets 1 L( ) y y ( x x ) j j j 1 1 j 1 An annua set of data was nvestgated n ths present study. The data sets were coected for a wnd farm whch s panned to be estabshed a regon wth hghest potenta of wnd power n Turkey. By usng wnd speed vaues obtaned for 10 m of attude, wnd speed vaues for 30 m of attude were predcted by SVMr technque. The resuts of predcton were compared by those obtaned from ANN, the most commony used technque n predcton of wnd speed and a comprehensve dscusson was made. Basc features of datas are shown n Tabe 1. In the study, the wnd speed measurement vaues for 10 m and 30 m attudes, coected n the frst 48 weeks of the year were used as earnng data whe the measurement vaues coected n 4 weeks were used as test data. 4. Fndngs Tabe 1: Summary of Data Sets Tran/Test Mn. vaue Max. vaue Average Number of weeks Tranng 3,8 6,07 4, Test 3,97 6,39 5,0 4 In ths secton, the resuts of anayses are presented. MLP whch s one of the most popuar and most successfu artfca neura network methods used forecastng studes and support vector regresson methods were used to forecast wnd speed. Forecastng resuts of two methods were compared. For wnd predcton, data of 48 weeks wnd speed measured at 10 and 30 meters were used for tranng, whe data of 4 weeks were used for testng. After many dfferent tras for each mode, poynoma kerne was seected for SVMr; where p and C (compexty coeffcent) were taken as 1. In MLP method, earnng coeffcent was L=0.3, moment was M=0., tranng number was N=500 and hdden ayer number was H=. Wnd speed forecastng resuts are presented n Fg. 3. (13) Fg. 3: Comparson of actua wnd speed wth SVMr and MLP. Performances of the methods empoyed were compared usng dfferent statstca measures. Mean Absoute Error (MAE), Root Mean Square Error (RMSE) and Correaton Coeffcent (r ) are among the wdey used measures that are based on the noton of mean error. Successes of SVMr and MLP methods 148

5 were compared usng the measures of Correaton Coeffcent, MAE and RMSE. Cacuated vaues reated to statstca measures are gven n Tabe. 5. Concusons Tabe : Comparson of Statstca Measures Method r MAE RMSE SVMr 0,9737 0,651 0,6079 MLP 0,970 0,355 0,6544 In ths study, SVMr s empoyed n weeky wnd speed forecastng and s compared wth the MLP mode. Fndngs of the research suggested that both methods are hghy successfu n wnd speed forecastng. When the methods are compared, the correaton between wnd speed at 30 m and predcton resut are very cose to each other for both technques. The resuts from ths study show that MAE and RMSE vaues s much smaer for SVMr technque. Thus, t can be stated that, n ths sampe study, SVMr shows a better performance compared to MLP. The study shows that both methods are qute successfu n the predcton of the wnd speeds and the predcted vaues are very cose to the rea measurements. For ths reason, t can be stated that wnd speed predctons for dfferent attudes made by SVMr and MLP may hep n decson makng for estabshment of wnd farms and n wnd farm pannng actvtes. 6. References [1] Eektrk Pyasası Sektör Raporu, Repubc of Turkey Energy Market Reguatory Authorty, 01 [] Strategc Pan, Turkey s Mnstry of Energy and Natura Sources, 014. [3] C. Ikc. Wnd energy and assessment of wnd energy potenta n Turkey. Renewabe and Sustanabe Energy Revews. 01, 16: [4] R. Ata. The current stuaton of wnd energy n Turkey. Journa of Energy, Hndaw Pubshng Co., 013. [5] M. Le, M. Shyan, J. Chuanwen, L. Hongng, and Z. Yan. A revew on the forecastng of wnd speed and generated power. Renewabe and Sustanabe Energy Revews. 009, 13: [6] V. Vapnk. The Nature of Statstca Learnng Theory, Sprnger, NY, [7] P.F. Pa, S.L. Yang, P.T. Chang. Forecastng output of ntegrated crcut ndustry by support vector regresson modes wth marrage honey-bees optmzaton agorthms. Expert Systems wth Appcatons. 009, 36: [8] M. Castro-Neto, Y.S. Jeong, M.K. Jeong, and L.D. Han. Onne-SVR for shortterm traffc fow predcton under typca and atypca traffc condtons. Expert Systems wth Appcatons. 009, 36: [9] S.H. Hsu, J.J.P. Hseh, T.C. Chh, and K.C. Hsu. A two-stage archtecture for stock prce forecastng by ntegratng sef-organzng map and support vector regresson. Expert Systems wth Appcatons. 009, 36: [10] C.L. Huang, and C.Y. Tsa. A hybrd SOFM SVR wth a fter-based feature seecton for stock market forecastng. Expert Systems wth Appcatons. 009, 36: [11] P.F. Pa, and C.S. Ln. A hybrd ARIMA and support vector machnes mode n stock prce forecastng. Orgna Research Artce Omega, 005, 6 (33): [1] M.A. Mohandes, T.O. Haawan, S. Rehman, and A.A. Hussan. Support vector machnes for wnd speed predcton. Renewabe Energy. 004, 9 (6): pp [13] S. Sacedo-Sanz, E.G. Ortz-Garcı, A.M. Perez-Bedo, J.A. Porta-Fgueras, L. Preto, D. Paredes, et a. Performance comparson of mutayer perceptrons and support vector machnes n a short-term wnd speed predcton probem. Neura Network Word. 009, 19 (1):