A Fusion Model for Day-Ahead Wind Speed Prediction based on the Validity of the Information

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1 A Fusion Model for Day-Ahead Wind Speed Predicion based on he Validiy of he Informaion Jie Wan 1,a, Wenbo Hao 2,b, Guorui Ren 1,c,Leilei Zhao 2,d, Bingliang Xu 2,e, Chengzhi Sun 2,f,Zhigang Zhao 3,g, Chengrui Lei 4,h, Yufeng Guo 1,i,Jinfu Liu 1,j and Daren Yu 1,k 1 Deparmen of Energy Science and Engineering, Harbin Insiue of Technology, Harbin, Heilongjiang, 151, China 2 Heilongjiang Elecric Power Research Insiue, Harbin, Heilongjiang, 151, China 3 Jilin Longyuan Wind Power Co.,Ld, Changchun, Jilin, 1322, China 4 Harbin Meeorological Bureau, Harbin, Heilongjiang, 1526, China Absrac In recen years wind power is a ho opic in he field of new energy. However, due o he larger flucuaions of wind power, which is a hrea o he safe operaion of he power grid, large-scale wind power inegraion has been grealy resriced. Therefore, accurae wind speed forecas has grea significance o wind power inegraion, especially he day-ahead wind speed forecas of 1-24 hours. For he sake of accurae wind speed forecas, he informaion efficiency of NWP and measured daa were aken ino accoun in his paper. Based on his, a fusion model was proposed. Experimens show ha he fusion sraegy can improve he accuracy of he day-ahead wind speed predicion resuls. Keywords - day-ahead wind speed predicion; informaion efficiency; fusion model; I. INTRODUCTION Wind power generaion is a ho issue of new energy power indusry in recen years [1-3]. However, he flucuaions of wind power make a severe impac on he safe and sable operaion of he power grid. Therefore accurae predicion of wind speed is he foundaion of wind power forecas. Paricularly he day-ahead wind speed forecas of 1-24 hours can guaranee he grid reasonable dispach, ensure power supply qualiy and help wind farms paricipaing in bidding for wind power inegraion. In recen years, many scholars began o focus on he day-ahead wind speed forecas by inegraing wind numerical weaher predicion and launched relaed research. The widely idea is using saisical models (such as neural neworks, ec.) o correc he resuls of numerical weaher predicion (NWP) in order o achieve he final forecas resuls. A.Vaccaroa used mesoscale numerical weaher predicion, local high-precision numerical weaher predicion, he measured wind speed and meeorological parameers o form a feaure vecor, designed Lazy learning algorihm based on k-neares Neighbour and aken he average of k mos similar vecor of hisory o forecas[4]. Federico combined Kalman filer and NWP for he dynamic correcion of numerical weaher predicion resul and indicaed ha Kalman filer algorihm can reduce sysemaic errors in NWP[5]. Cai Qizhen used hisorical daa of NWP and oher relaed daa as inpu, he measured daa as he oupu o rain he neural nework o obain correcion model; hen he dae of NWP was inpued and he correced NWP forecas resuls were achieved [6]. However, he curren mehods do no ake ino accoun he informaion efficiency of numerical weaher predicion and measured daa. The final predicion resul was a simple synhesis of he predicion resuls of NWP and saisical mehod, which has he same predicion ime. In fac, he predicion ime of NWP and saisical mehod are significan differen. So he forecas resuls is no accurae. In order o solve he above menioned problems, he informaion efficiency of NWP and measured daa are aken ino accoun in his paper. Firsly, he lengh of predicion sep of NWP and measured daa are analyzed respecively. Secondly, fusion mehod is seleced based on he informaion efficiency of NWP and measured daa. Finally, he finale predicion resul is achieved by fusion mehod. Figure 1 is he skech map of fusion model. Fig.1The skech map of fusion model DOI 1.513/IJSSST.a ISSN: x online, prin

2 II. THE INFORMATION EFFICIENCY OF MEASURED DATA The informaion efficiency of measured daa need o be analyzed before seing up he saisical model. There are many facors (such as emperaure, pressure, surface roughness, amospheric circulaion, ec.) have influence on wind speed and he mechanisms are complex. So he wind speed signal exhibis a srong muli-scale feaure. Namely ha he signal frequencies generaed by differen facor are differen, so wind speed ime series can be regarded as he superposiion of differen frequency signals. For he sake of analyzing he informaion efficiency of measured daa, he following analysis seps are proposed in his paper: Sep-1: Based on he frequency domain characerisics of muli-scale wind speed signal, he original wind speed signal was decomposed ino sub-sequences of differen frequencies by using wavele decomposiion. Due o decomposiion level affecing he final scale signal frequency, signal is usually divided ino hree layers according o he previous experience. Figure 2 is original ime series of he wind speed. Figure 3 is he resuls of hree wavele decomposiion. To faciliae he laer modeling, he high-frequency secion in he firs sub-series is recorded as {} series, he high-frequency secion in he second sub-series is recorded as {} series, he high-frequency secion in he hird sub-series is recorded as {}series, and he low-frequency secion in he hird sub-series is recorded as {} series. Sep-2: The informaion efficiency in each frequency-domain sequence was measured by using he auocorrelaion and muual informaion analysis. Afer he analysis of predicabiliy, he lengh of predicion sep in each frequency-domain sequence could be achieved according o he resul of analysis. This procedure is useful o deermine he number of inpu variables ha can influence he forecas variable as all he inpus are seleced from he wind speed poins and he forecas horizon of each sub-series can also be analyzed hrough he analysis. Then he model archiecure and forecas horizon of he sub-series in each frequency can be achieved. The auocorrelaion funcion is useful o achieve he dependencies among he daa o idenify he number of inpus and forecas horizons. Le {x,=1:n }is a ime series, he auocorrelaion funcion is: k n k 1 ( x x)( x n k 1 2 ( x x) k x) where x is he mean value of {x,=1:n }. Figure 4 is auocorrelaion funcion values wih he change of delay number k in each frequency-domain sequence. Generally speaking, when he auocorrelaion funcion value exceed.8, here is a srong correlaion beween he measured daa. Namely ha he higher he credibiliy of he forecas resuls. Sudying on he low-frequency secion in he hird sub-series{}, we can find ha he auocorrelaion funcion decreased monoonically wih he change of delay number. If we ake he hreshold value of.8, he auocorrelaion lengh of his layer is 4 hour. Similarly, he predicable ime in he sub-series{},{} and {} are 1 hour,.4 hour and.2 hour. Based on he analysis of informaion efficiency, i revealed ha he predicabiliy of each frequency sub-series are differen. The predicabiliy of low-frequency sub-series is sronger han ha of high-frequency and secondary high-frequency sub-series Wind speed (m/s) Time (1 minues) Fig.2 Original ime series of he wind speed Fig.3 Decomposiion resuls of he original series. DOI 1.513/IJSSST.a ISSN: x online, prin

3 Sep-3: Saisical models are esablished in each frequency-domain sequence afer he analysis of informaion efficiency. In his paper, suppor vecor machine regression model (SVR) is seleced o make predicion. The inpu of he saisical model for each layer is seleced according o he correlaion lengh of each layer. Assume i is ime, he curren wind speed v() and he L-1 pas saes of wind speed are considered as he inpu vecors, while L is he auocorrelaion lengh of each sub-series. Meanwhile, he predicable ime of saisical model for each layer is also deermined by he correlaion lengh of each layer. Sep-4: Afer geing he predicion resuls of each sub-series, we can use wavele reconsrucion algorihm o obain he final wind speed by muli-scale synhesis. The SVR model in each sub-series jus forecas he wind speed which is in he range of predicabiliy. For he high-frequency secion, which is only responsible for he predicion resul in nex.2 hours, for oher frequency secions, using he same mehod. When he predicion ime exceeds one hour, he predicion resul is only obained from he low frequency secion. Through above analysis of informaion efficiency for measured daa, i can be found ha he predicion resul of saisical mehods is high credible wihin 4 hours. When he predicion ime exceeds 4 hours, he credibiliy of predicion resul begin o decrease. Fig.4 Auocorrelaion funcion values wih he change of delay number k in each frequency-domain sequence. (a){} series.(b){} series.(c){} series.(d){} Fig.5 Anomaly correlaion coefficien of Norhern Hemisphere 5hPa III. THE INFORMATION EFFICIENCY OF NWP The basic principle of numerical weaher predicion is: Using mass conservaion, momenum conservaion, energy conservaion, and conservaion of waer vapor o esablish he basic amospheric equaions, meanwhile urbulence models are used o close he equaions. Then using he iniial and boundary condiions o solve he amospheric equaions in order o forecas he fuure sae of he amosphere condiions. The ypical forecas model is WRFa, using hree nesed grids, he calculaed horizons are: 27km 27km, 9km 9km, 3km 3km. The oupu of NWP sysem are average value of ime and space for each compuaional grid, raher han insananeous value. Therefore, he principle of calculaion deermines he predicion abiliy of NWP, which only conains he long-period componen raher han shor-cycle ransien componen, he lengh of predicion sep is 1h. The effec of insananeous forecass is no ideal. Many scholars sudied he predicabiliy problem of NWP. Smagorinsky s experimens showed ha,wih he increase of predicion ime, he changes of forecas error is a nonlinear process. Poined ou ha he predicabiliy ime of NWP is abou 8 days [7]. Loren proved ha NWP can predic amospheric condiions wihin a week [8]. Many sudies have fully confirmed ha 2 weeks predicion resuls of NWP is high reliabiliy [9]. A presen, European Cenre for Medium-Range Weaher Forecass (ECMWF) can provide 7-9 days predicion resuls of amosphere condiions, meanwhile, Naional Ceners for Environmenal Predicion (NCEP) can provide 6 8 days predicion resuls [1]. Figure 5 (The verical axis represens he correlaion coefficien and he horizonal axis represens predicion ime) is he T42L9 mode s anomaly correlaion coefficien of Norhern Hemisphere 5hPa heigh. From he graph we DOI 1.513/IJSSST.a ISSN: x online, prin

4 can see he anomaly correlaion coefficien of he fourh day is.7. As a general rule, he predicion resuls is crediable when he anomaly correlaion coefficien exceeds.6. Above sudy resuls show ha he predicable abiliy of NWP is srong, he long-erm predicion resuls of NWP is crediable [11]. When using SVR and numerical weaher predicion (NWP) forecas wind speed, he average relaive error and he Mean Square Error (MSE) of predicion resuls are shown in Figure 6 and Figure 7. I can be found ha when he predicion ime is in he range of four hours, he predicion error of SVR and NWP have lile difference, bu once he predicion ime exceeds 4 hours, boh he MSE and MAE of NWP are beer han ha of SVR. Through he analysis of informaion efficiency for NWP, we can find ha NWP has higher predicable abiliy, namely ha he predicion resuls of long-erm are beer han insananeous forecas. 4% 35% 3% 25% 2% 15% 1% 5% SVM NWP Fig.6 The average relaive error curve Fig.7 The mean square error curve IV. FUSION MODEL AND EXPERIMENTAL RESULTS Above analysis shows ha: Using saisical model (which is based on measured daa) o predic wind speed, when he predicion ime exceeds 4 hours, he correlaion funcion value of low frequency secion is less han.8 and decreases wih he increase of prediced ime, so he credibiliy of predicion resuls begins o decrease. However, NWP has higher predicable abiliy, namely ha he predicion resuls of long-erm are beer han insananeous forecas. Therefore we can use he differences and complemenariies beween NWP and saisical model o esablish fusion model. The fusion sraegy is: Wind speed forecas wihin four hours can be obained from NWP and hisorical wind speed, he specific approach is using geneic algorihms o synhesize he predicion resuls of NWP and saisical model. When he predicion ime exceeds 4 hours, wind speed predicion resuls mosly rely on NWP. In order o verify he effeciveness of he fusion model, he day-ahead wind speed predicion resul of he fusion model is compared wih acual wind speed, which is shown in Figure 8. As can be seen, here is a good coincidence degree beween he day-ahead wind speed predicion resul of he fusion model and acual wind speed. 4% 35% 3% 25% 2% 15% 1% 5% Fig.8 The ime series graph of experimenal resuls and acual wind speed Fig.9 The average relaive error variaion curve for differen models DOI 1.513/IJSSST.a ISSN: x online, prin

5 To furher illusrae he effeciveness of he fusion sraegy, comparing he predicion resul of fusion model wih he resul of saisical model on he same es se. The average relaive error variaion curve for differen models is shown in Figure 9. By comparison,i can be seen in he enire es se he predicion error of fusion model is smaller han ha of saisical model, which fully demonsrae he effeciveness of he fusion model and can improve he accuracy of predicion resuls. V. CONCLUSION The day-ahead wind speed forecas of 1-24 hours can guaranee he grid reasonable dispach, ensure power supply qualiy and help wind farms paricipaing in bidding for wind power inegraion. Afer considering he informaion efficiency of NWP and measured daa, a fusion model is proposed in his paper. (1). Based on he analysis of informaion efficiency, i revealed ha he predicable ime of NWP and saisical models (which are based on measured daa) are differen. The shor-erm predicion resuls of saisical models is high credible, while as for NWP, he predicion resuls of long-erm are beer han insananeous forecas. (2). The fusion modrl, which is proposed in his paper, akes full advanage of he differences and complemenariy beween NWP and saisical. Experimens show ha he fusion model can improve he accuracy of he day-ahead wind speed predicion resuls. forecasing [J]. Elecric Power Sysems Research, 81(3): [5] Cassola F, Burlando M. Wind speed and wind energy forecas hrough Kalman filering of Numerical Weaher Predicion model oupu [J]. Applied energy, 99: [6] Zhenqi Cai. Correced Numerical Weaher Predicion-BP Neural Nework Based Wind Power Shor- erm Predicion Research. [D]. Zhejiang Universiy, 212.(in Chinese) [7] Smagorinsky, J., Problems and promises of deerminisic exened range forecasing, Bull. Amer.Meeor. Soc. 5, ,1969. [8] Lorenz E N. A sudy of he predicabiliy of a 28 variable amospheric model [J]. Tellus, 17(3): [9] Jifan Chou. Weaher and climae predicabiliy.[j] Meeorological Science and Technology Progress, 1(2): 12r14.(in Chinese)211 [1] Zhihai Zhen. Operaional echnology research of 6-15 days numerical weaher predicion based on predicable componens [D]. Lanzhou Universiy, 21.(in Chinese) [11] Zhijin Li. Analysis of spaial and emporal dependence of he acual forecas predicabiliy [J]. Amospheric Science, 2(3): (in Chinese) CONFLICT OF INTEREST The auhors confirm ha his aricle conen has no conflics of ineres. ACKNOWLEDGMENT This work was suppored by Naional Program on Key Basic Research Projec (973 Program) under Gran, 212CB21521, and by Heilongjiang Elecric Power Company Science and Technology Key Projecs ([214] 19). REFERENCES [1] Roulson M S, Kaplan D T, Hardenberg J, e al. Using medium-range weaher forcass o improve he value of wind energy producion [J]. Renewable Energy, 28(4): [2] Alboyaci B, Dursun B. Elecriciy resrucuring in Turkey and he share of wind energy producion [J]. Renewable Energy, 33(11): [3] Wichser C, Klink K. Low wind speed urbines and wind power poenial in Minnesoa, USA [J]. Renewable Energy, 33(8): [4] Vaccaro A, Mercogliano P, Schiano P, e al. An adapive framework based on muli-model daa fusion for one-day-ahead wind power DOI 1.513/IJSSST.a ISSN: x online, prin