SCREENING OF TOPOGRAPHIC FACTOR ON WIND SPEED ESTIMATION WITH NEURAL NETWORK ANALYSIS

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1 The Seventh Asia-Pacific Conference on Wind Engineering, November 8-12, 2009, Taipei, Taiwan SCREENING OF TOPOGRAPHIC FACTOR ON WIND SPEED ESTIMATION WITH NEURAL NETWORK ANALYSIS Fumiaki Nagao 1 Minoru Noda 2 and Takaaki Kusuhara 3 1 Professor, Department of Civil and Environmental Engineering, The University of Tokushima, 2-1,Minami-Josanjima, Tokushima, Japan, fumi@ce.tokushima-u.ac.jp 2 Associate Professor, Department of Civil and Environmental Engineering, The University of Tokushima, 2-1,Minami-Josanjima, Tokushima, tarda@ce.tokushima-u.ac.jp 3 Graduate Student, Department of Civil and Environmental Engineering, The University of Tokushima, 2-1,Minami-Josanjima, Tokushima ABSTRACT Author s previous studies concerning to the topographic factor analysis of wind properties by using multi regression analysis and the information of upper air wind gave precise estimation results. In this study, a neural network (NN) was adopted instead of the multi regression analysis. The effects of the combination of topographic factors and the sampling index of training data for NN on the estimation of wind properties were investigated. The reduction of topographic factors and the use of appropriate sampling index of training data for NN could introduce the accurate estimation of wind properties. KEYWORDS: TOPOGRAPHIC FACTOR ANALYSIS, NEURAL NETWORK, UPPER AIR WIND SPEED Introduction The estimation of wind properties at a specific site is important to evaluate the wind load on a structure, the diffusion of air pollutant and the wind energy, et al. Wind maps obtained from general meteorological stations with long-term records will give the preliminary estimation of wind speed for the specific location. However, the wind speed given by the wind maps was not always sufficient for an accurate estimation because of the complex terrain properties around the location. Therefore, lots of studies concerning to it have been carried out by the field observations (Teunissen (1983), Utsunomiya et al (1989)), wind tunnel tests using scaled topographic model (Cook et al (1977/1978), Utsunomiya et al (1992)), theoretical and numerical analyses (Jackson & Hunt (1975), Murakami et al (2003)), and regression analyses (Murakami et al (1983), Utsunomiya et al (1998), Nagao et al(2005)). In this study, the improvement of the estimation accuracy of wind properties by using a neural network (NN) analysis with topographic factors (Noda et al (2006)) was attempted by the introduction of upper air wind information, which was chosen as the representative of the wind properties at the specific site, by the reduction of topographic factors and by the use of training data based on appropriate sampling index. Observation Sites and Wind Data Sample data of surface wind speed and direction were obtained from the records of AMeDAS (Automated Meteorological Data Acquisition System) observatories, which were developed all over the Japanese Island as a meteorological network system. There are 43

2 observatories of AMeDAS in Shikoku Island, from which typical 30 observatories were chosen for the analysis as shown in Fig.1. Sample data of upper wind properties in the 850 hpa pressure surface were obtained from RANAL (Regional objective analysis dataset), which was used for the weather prediction in Japan, whose mesh size is about 20km. The upper wind properties for each AMeDAS site were interpolated from surrounding grid s values. The mean wind speed and its relative frequency of occurrence of wind direction for 16 azimuths in each station were calculated by using mean wind speeds and directions averaged for ten minutes in every twelve hour, that is, 00UTC and 12UTC, during three years from 1997 to 1999 and in every six hour, 00, 06, 12 and 18UTC, during five years from 2002 to The ratios of surface wind speeds to upper wind speeds for 16 wind directions at the same time were used as the output in the NN analysis using topographic factors. Topographic Factors and Neural Network Topographic factors used here were followings; 1) Reduced distance from sea [RDS, m], RDS = 60,000 m - the shortest distance from the sea to the site. If there is no sea, 0 km is set for RDS, therefore, the larger RDS should be corresponding to the higher wind speed. 2) Reduced distance from obstacle [RDO, m], RDO = 60,000 m - the shortest distance, r1, from the obstacle region given by shaded area shown in Fig. 2, where θ = 0.15 (rad.) and r = 10,000 m were chosen. If there is no obstacle, 0 km is set for RDS, therefore, the larger RDS implies the larger sheltering effects. 3) Ratio of obstacle [RO, %], ratio of obstacle region shown in Fig. 2 to the fan-shape area with the radius of r2 corresponding to the distance between the highest altitude point and the site, where the area is weighted by the distance to the site taking into account of the influence magnitude of the distance. 4) Mean altitude [MA, m], mean altitude is an average of the altitude divided in the distance to the site, since the influence of altitude is small as the site leaving far away. 5) Effect of Sheltering [EOS], sheltering effect is summation of the weighted area, where forest, building et al are belong to rank 3, which has weighting factor of 3, grassy plain, farmland et al are classified into rank 2 with weighting factor 2, and rank 1 with factor 1 is consisted with sea, lake and river. 6) Effect of Convergence [EOC], convergence effect is calculated by using the sheltering effect, EOS as follows, {EOS (right hand side) + EOS (left hand side)} {2 EOS (upstream) + EOS (downstream)}. 7) Degree of valley [DV], degree of valley is defined the ratio of the depth of valley to the width of the valley, the depth and width of valley were estimated by the envelope of the terrain within the radius of 5 km. 8) Degree of mountain [DM], degree of the mountain is the same definition for DV, however, definition of mountain is the opposite of the valley. 9) The altitude of the site, [ALT, m], and 10) the height of the anemometer above ground, [H, m] were also used in the analysis. Pt. S Figure 1: Location of Analyzed AMeDAS Observatories r0 r A θ: Inclination angle defining obstacle region, θ=0.15 (rad) for RDO and θ=0.05 for RO, respectively r1: Minimum distance to obstacle region r2: Distance to the maximum altitude r0: 10 km in the analysis RO = A / r + A / r / A / r A1 A2 A A: Fan shaped area within r2 Figure 2: Definition of Obstacle Region

3 Topographic factors were obtained from the altitude data of the Grid Information System Data for Japan where altitude and land information were recorded at an interval of about 50m, respectively. The neural network used here was constituted of single hidden layer with two units. In order to increase the estimation accuracy, it is very important how to select the training data. Four kinds of sampling index for the selection of training data were introduced, that is, 1) wind speed on the grand [GWS], 2) the correlation between upper and surface wind speed [CWS], 3) the wind speed ratio between upper and surface wind [RWS], and 4) the incidence of the same wind direction between upper and surface winds [IWD]. In the analysis, the number of training data was increased from reliable 20 data to all data 480 (=16 azimuths 30 sites)) with the interval of 20. Results and Discussions In Figure 3, the correlation coefficients of surface wind speeds between the observation and the estimation obtained from four different sampling indexes were shown by the numbers of the training data set, where the topographic factor, Ratio of obstacle [RO] did not used. Figure (a) shows the results for all data set including the training data and Figure (b) gives the results of estimated wind speeds. From Figure 3(a), the increase of the training data set gives the more accurate estimation for all data set in four different indexes due to the increase of training data themselves. On the other hand, as shown in Figure 3(b), the correlations of the estimated wind speeds show different distributions depended on the sampling indexes of training data, where the training data chosen by the incidence of the same wind direction, IWD, give the highest accuracy. In the Figure 4, the rest square errors of surface wind speeds between the observation and the estimation obtained from four different sampling indexes were shown against the numbers of the training data set. The rest square errors of the estimated wind speeds become small in the case of the sampling indexes of the correlation between upper and surface wind speed, CWS, and the incidence of the same wind direction, IWD. The scatter diagram for observed and estimated surface wind speeds obtained in 340 training data (70% of all data) selected by IWD is given in Figure 5. The good agreement of surface wind speeds between estimations and observations was obtained. Therefore in the NN analyses here after, the training data was selected by the index of the IWD. Table 1 shows the effects of the removal of one topographic factor in the analyses on the averaged correlation coefficients of surface wind speeds between the observation and the estimation obtained over 23 analyses changing the numbers of training data from 20 to 460 with the interval of 20. In the case of the removal of the factor, Reduced distance from obstacle [RDO], the mean correlation coefficient shows the highest value, R= Furthermore, the analyses for the removal of two topographic factors were carried out, where one removal factor was fixed to the Reduced distance from obstacle [RDO]. Two combinations of the removal of topographic factors showed the improvement of estimation, that is, one was RDO and RO, the other was RDO and MA. The effects of the removal of those topographic factors on the correlation coefficients and the rest square errors of surface wind speeds between the estimation and the observation are shown in Figure 6 and Figure 7, respectively. From Figure 6, the removal of two topographic factors gave higher correlations where the training data were less than 260. Moreover, at the training data number of 360, the low correlation was scored in the case of the removal of RDO, while the removal of two topographic factors kept higher correlations. For the rest square errors, as shown in Figure 7, the poor estimation was also obtained at the training data number of 360 in the case of the removal of RDO, while the removal of two topographic factors kept small errors. The scatter

4 diagram of estimated surface wind speeds without RDO, RDO + RO and RDO + MA at the training data number of 360 were shown in Figure 8 (a), (b) and (c), respectively. It is clear that estimated values without RDO shows extremely overestimated values, however, those without RDO + RO and RDO + MA show good correlations. For the worst estimated observatory site in Figure 8, where indicated in Figure 1 as Pt. S, the radar chart of estimated and observed surface wind speeds without RDO, RDO + RO and RDO + MA at the training data number of 360 were shown in Figure 9 (a), (b) and (c), respectively. The wind speed of SWS which is parallel to the coastline at Pt. S was extremely overestimated without RDO, while those of the removal of two topographic factors gave closer estimations. From these results, the removal of two topographic factors, RDO and RO or RDO and MA, gave the most precise estimation in the analyses used here. Table 1: Averaged Correlation Coefficients of Surface Wind Speeds between Observation and Estimation by the Removal of One topographic Factor Removed Topographic Factor RDS RDO RO MA EOS EOC DV DM - R Rest Square Error, e 2 IWD RWS CWS GWS IWD RWS CWS GWS IWD RWS CWS GWS (a) All Data Including Training Data (b) Estimation Figure 3: Correlation Coefficients between Estimated and Observed Surface Wind Speeds Using Four Different Sampling Indexes for the Selection of Training Data Figure 4: Rest Square Errors between Estimated and Observed Surface Wind Speeds Using Four Different Sampling Indexes for the Selection of Training Data Estimation (m/s) Rest Square Error, e 2 Training Data Estimation Figure 5: Scatter Diagram Obtained at 340 Training Data Selected by the Incidence of Wind Direction Without RDO + Without RDO+RO Without RDO+MA Figure 6: Correlation Coefficients without RDO, RDO+RO and RDO+MA Without RDO + Without RDO+RO Without RDO+MA Figure 7: Rest Square Errors without RDO, RDO+RO and RDO+MA

5 Estimation (m/s) Estimation (m/s) Estimation (m/s) (a) without RDO (b) without RDO and RO (c) without RDO and MO Figure 8: Scatter Diagram of Surface Wind Speeds Obtained at 360 Training Data (a) without RDO (b) without RDO and RO (c) without RDO and MA Figure 9: Radar Chart of Estimated ( ) and Observed ( ) Surface Wind Speeds for 360 Training Data at Pt. S In order to confirm the effects of topographic factors on the surface wind properties, the topographic factors were changed to random variables, and then the NN analyses were carried out. The correlation coefficients and the rest square errors based on the normal topographic factors and random variables were indicated in Figure 10 and 11, where Figures (a), (b) and (c) correspond to estimated wind speeds, training data and all data, respectively. From these figures, the wind speeds obtained from the random data instead of topographic factors show extreme poor correlations and large errors for not only estimated one but also training data. Therefore, it is apparent that topographic factors used here contribute to the local wind properties. Conclusions The reduction of topographic factors and the use of appropriate sampling index of training data for the neural network analysis, NN could introduce the accurate estimation of surface wind properties by using the introduction of upper air wind information as the representative of the wind properties at the specific site. It became also clear that topographic factors used here affect properly on the local wind properties from the substitution of random variables for the topographic factors. References Cook, N.J., Coulson, B.H. Mckay, W., (1977/1987), Wind conditions around the Rock of Gibraltar. J. Wind Eng. Ind. Aerodyn., Vol. 2,

6 (a) Estimation (b) Training Data (c) All Data Figure 10: Correlation Coefficients Based on the Normal Topographic Factors ( ) and Random Variables ( ) Rest Square Error, e 2 Rest Square Error, e 2 Rest Square Error, e 2 (a) Estimation (b) Training Data (c) All Data Figure 11: Rest Square Errors Based on the Normal Topographic Factors ( ) and Random Variables ( ) Jackson, P.S., Hunt, J.C.R., (1975), Turbulent wind over a low hill. Quart. J. R. Met. Soc., Vol.101, Murakami, S., Komine, H., (1983), Prediction method for surface wind velocity distribution by means of regression analysis of topographic effects on local wind speed. J. Wind Eng. Ind. Aerodyn., Vol. 15, Murakami, S., Otsuka, et al, (2003), CFD prediction of flow over complex terrain using local area wind energy prediction system (LAWEPS). Proc. 11th Int. Cong. on Wind Eng., Lubbock, Texas, USA, Vol. 2, Nagao, F, Noda, M, et al, (2005), Regression analysis of local wind properties taking into account local terrain factors and upper air information. Proc. 18th KKCNN Symp. on Civil Eng. 2005, Kaohsiung, Taiwan, Noda, M, Nagao, F, Chikae, Y, and Utsunomiya, H, (2006), Study on estimation of ground wind based on the upper wind information. Proc. 19th National Symp. on Wind Eng. 2006, (in Japanese). Teunissen, H.W., (1983), Wind tunnel and full-scale comparisons of mean wind flow over an isolated low hill. J. Wind Eng. Ind. Aerodyn., Vol. 15, Utsunomiya, H., Nagao, F., Yoshimura, S., (1989), Wind tunnel and full-scale comparisons on the change of local wind characteristics due to an open cut. J. Wind Eng. Ind. Aerodyn., Vol. 32, Utsunomiya, H., Nagao, F., Saito, Y., (1992), Estimation of local strong wind around small peninsula-like terrain. J. Wind Eng. Ind. Aerodyn., Vol. 41, Utsunomiya, H., Nagao, F., Urakami, I., (1998), Regression analysis of local wind properties with local topographic factors. J. Wind Eng. Ind. Aerodyn., Vol ,

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