The Experts below are selected from a list of 2367 Experts worldwide ranked by ideXlab platform
Masayoshi Kojima - One of the best experts on this subject based on the ideXlab platform.
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solar cycle evolution of the solar Wind Speed Distribution from 1985 to 2008
Journal of Geophysical Research, 2010Co-Authors: M Tokumaru, Masayoshi Kojima, K FujikiAbstract:[1] The evolution of solar Wind Speed Distribution during the period 1985–2008, which covers two solar cycles (22 and 23), has been investigated using multistation interplanetary scintillation (IPS) measurements at 327 MHz. The results obtained here clearly demonstrate that fast (slow) Wind areas increase (decrease) systematically as the solar activity diminishes, reaching the maximum (minimum) value at the minimum phase. The intermediate-Speed Wind areas appear to remain constant over the solar cycle. The preponderance of slow Wind at low latitudes was confirmed from our IPS observations throughout the period, while a slight increase in the fast Wind area was revealed in the declining to minimum phases. In contrast, the high-latitude solar Wind was mostly dominated by the fast Wind except for a few years around the maxima. An important point to note is the clear difference in the solar Wind Speed Distribution between the 1996 and 2008 minima. The fast Wind areas in 2008 showed a marked increase at low latitudes, which is consistent with in situ observations at 1 AU, and a distinct decrease at high latitudes, resulting in a net decrease at all latitudes, as compared with those in 1996. This difference is ascribed to the weaker polar fields during the 2008 minimum. An excellent positive (negative) correlation between fast (slow) Wind areas and the polar fields is revealed from a comparison between IPS and magnetograph observations. The results obtained here suggest a strong control of the Sun's polar field in determining the solar Wind acceleration and structure.
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Solar cycle dependence of global Distribution of solar Wind Speed
Space Science Reviews, 1990Co-Authors: Masayoshi Kojima, Takakiyo KakinumaAbstract:A review is given of observational results concerning the solar cycle dependence of the global structure of solar Wind Speed Distribution during the years from 1973 to 1987. Since observations of solar Wind Speed in 3-dimensional space can only be made by the interplanetary scintillation method which has been carried out over one sunspot activity cycle since the early 1970's, the review is based on IPS observations. The low-Speed regions tend to be distributed along neutral lines which are derived on the source surface, so comparisons between Speed Distribution and the neutral line are discussed.
Takakiyo Kakinuma - One of the best experts on this subject based on the ideXlab platform.
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Solar cycle dependence of global Distribution of solar Wind Speed
Space Science Reviews, 1990Co-Authors: Masayoshi Kojima, Takakiyo KakinumaAbstract:A review is given of observational results concerning the solar cycle dependence of the global structure of solar Wind Speed Distribution during the years from 1973 to 1987. Since observations of solar Wind Speed in 3-dimensional space can only be made by the interplanetary scintillation method which has been carried out over one sunspot activity cycle since the early 1970's, the review is based on IPS observations. The low-Speed regions tend to be distributed along neutral lines which are derived on the source surface, so comparisons between Speed Distribution and the neutral line are discussed.
Dalibor Petkovic - One of the best experts on this subject based on the ideXlab platform.
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a combined method to estimate Wind Speed Distribution based on integrating the support vector machine with firefly algorithm
Environmental Progress, 2016Co-Authors: Abdullah Gani, Shahaboddin Shamshirband, Kasra Mohammadi, Dalibor Petkovic, Torki A Altameem, Sudheer ChAbstract:A new hybrid approach by integrating the support vector machine (SVM) with firefly algorithm (FFA) is proposed to estimate shape (k) and scale (c) parameters of the Weibull Distribution function according to previously established analytical methods. The extracted data of two widely successful methods utilized to compute parameters k and c were used as learning and testing information for the SVM-FFA method. The simulations were performed on both daily and monthly scales to draw further conclusions. The performance of SVM-FFA method was compared against other existing techniques to demonstrate its efficiency and viability. The results conclusively indicate that SVM-FFA method provides further precision in the predictions. Nevertheless, for daily estimations, the applicability of proposed method could not be feasible owing to high day-by-day fluctuations of parameters k, whereas the results of monthly estimation are completely appealing and precise. In summary, the SVM-FFA is a highly viable and efficient technique to estimate Wind Speed Distribution on monthly scale. It is expected that the proposed method would be profitable for Wind researchers and experts to be used in many practical applications, such as evaluating the Wind energy potential and making a proper decision to nominate the optimal Wind turbines. © 2015 American Institute of Chemical Engineers Environ Prog, 2015
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application of extreme learning machine for estimation of Wind Speed Distribution
Climate Dynamics, 2016Co-Authors: Shahaboddin Shamshirband, Kasra Mohammadi, Chong Wen Tong, Dalibor Petkovic, Emilio Porcu, Ali Mostafaeipour, Sudheer Ch, Ahmad SedaghatAbstract:The knowledge of the probabilistic Wind Speed Distribution is of particular significance in reliable evaluation of the Wind energy potential and effective adoption of site specific Wind turbines. Among all proposed probability density functions, the two-parameter Weibull function has been extensively endorsed and utilized to model Wind Speeds and express Wind Speed Distribution in various locations. In this research work, extreme learning machine (ELM) is employed to compute the shape (k) and scale (c) factors of Weibull Distribution function. The developed ELM model is trained and tested based upon two widely successful methods used to estimate k and c parameters. The efficiency and accuracy of ELM is compared against support vector machine, artificial neural network and genetic programming for estimating the same Weibull parameters. The survey results reveal that applying ELM approach is eventuated in attaining further precision for estimation of both Weibull parameters compared to other methods evaluated. Mean absolute percentage error, mean absolute bias error and root mean square error for k are 8.4600 %, 0.1783 and 0.2371, while for c are 0.2143 %, 0.0118 and 0.0192 m/s, respectively. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate Weibull k and c factors.
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adaptive neuro fuzzy approach for estimation of Wind Speed Distribution
International Journal of Electrical Power & Energy Systems, 2015Co-Authors: Dalibor PetkovicAbstract:Abstract Probability Distribution of Wind Speed is very important information needed in the assessment of Wind energy potential. For this reason, a large number of studies have been published concerning the use of a variety of probability density functions to describe Wind Speed frequency Distributions. Two parameter Weibull Distribution is widely used and accepted method. In this investigation adaptive neuro-fuzzy inference system (ANFIS) was used to predict the probability density Distribution of Wind Speed. The estimation and prediction results of ANFIS model are calculated using three statistical indicators i.e. root means square error, coefficient of determination and Pearson coefficient. The results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. Moreover, the results indicate that proposed ANFIS model can adequately predict the probability Distribution of Wind Speed.
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generalized adaptive neuro fuzzy based method for Wind Speed Distribution prediction
Flow Measurement and Instrumentation, 2015Co-Authors: Dalibor Petkovic, Shahaboddin Shamshirband, Chong Wen Tong, Eiman Tamah AlshammariAbstract:The probabilistic Distribution of Wind Speed is one of the important Wind characteristics for the assessment of Wind energy potential and for the performance of Wind energy conversion systems. When the Wind Speed probability Distribution is known, the Wind energy Distribution can easily be obtained. Therefore, the probability Distribution of Wind Speed is a very important piece of information needed in the assessment of Wind energy potential. For this reason, a large number of studies have been published concerning the use of a variety of probability density functions to describe Wind Speed frequency Distributions. Two parameter Weibull Distribution is widely used and accepted method. Artificial neural networks (ANN) can be used as an alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, was used to predict the annual probability density Distribution of Wind Speed. The simulation results presented in this paper show the effectiveness of the developed method.
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an appraisal of Wind Speed Distribution prediction by soft computing methodologies a comparative study
Energy Conversion and Management, 2014Co-Authors: Dalibor Petkovic, Shahaboddin Shamshirband, Nor Badrul Anuar, Hadi Saboohi, Ainuddin Wahid Abdul Wahab, Milan Protic, E Zalnezhad, Seyed Mohammad Amin MirhashemiAbstract:The probabilistic Distribution of Wind Speed is among the more significant Wind characteristics in examining Wind energy potential and the performance of Wind energy conversion systems. When the Wind Speed probability Distribution is known, the Wind energy Distribution can be easily obtained. Therefore, the probability Distribution of Wind Speed is a very important piece of information required in assessing Wind energy potential. For this reason, a large number of studies have been established concerning the use of a variety of probability density functions to describe Wind Speed frequency Distributions. Although the two-parameter Weibull Distribution comprises a widely used and accepted method, solving the function is very challenging. In this study, the polynomial and radial basis functions (RBF) are applied as the kernel function of support vector regression (SVR) to estimate two parameters of the Weibull Distribution function according to previously established analytical methods. Rather than minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound, so as to achieve generalized performance. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved using the SVR approach compared to other soft computing methodologies.
K Fujiki - One of the best experts on this subject based on the ideXlab platform.
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solar cycle evolution of the solar Wind Speed Distribution from 1985 to 2008
Journal of Geophysical Research, 2010Co-Authors: M Tokumaru, Masayoshi Kojima, K FujikiAbstract:[1] The evolution of solar Wind Speed Distribution during the period 1985–2008, which covers two solar cycles (22 and 23), has been investigated using multistation interplanetary scintillation (IPS) measurements at 327 MHz. The results obtained here clearly demonstrate that fast (slow) Wind areas increase (decrease) systematically as the solar activity diminishes, reaching the maximum (minimum) value at the minimum phase. The intermediate-Speed Wind areas appear to remain constant over the solar cycle. The preponderance of slow Wind at low latitudes was confirmed from our IPS observations throughout the period, while a slight increase in the fast Wind area was revealed in the declining to minimum phases. In contrast, the high-latitude solar Wind was mostly dominated by the fast Wind except for a few years around the maxima. An important point to note is the clear difference in the solar Wind Speed Distribution between the 1996 and 2008 minima. The fast Wind areas in 2008 showed a marked increase at low latitudes, which is consistent with in situ observations at 1 AU, and a distinct decrease at high latitudes, resulting in a net decrease at all latitudes, as compared with those in 1996. This difference is ascribed to the weaker polar fields during the 2008 minimum. An excellent positive (negative) correlation between fast (slow) Wind areas and the polar fields is revealed from a comparison between IPS and magnetograph observations. The results obtained here suggest a strong control of the Sun's polar field in determining the solar Wind acceleration and structure.
Ayhan Albostan - One of the best experts on this subject based on the ideXlab platform.
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seasonal and yearly Wind Speed Distribution and Wind power density analysis based on weibull Distribution function
International Journal of Hydrogen Energy, 2015Co-Authors: Levent Bilir, Mehmet Imir, Yilser Devrim, Ayhan AlbostanAbstract:Abstract Wind energy, which is among the most promising renewable energy resources, is used throughout the world as an alternative to fossil fuels. In the assessment of Wind energy for a region, the use of two-parameter Weibull Distribution is an important tool. In this study, Wind Speed data, collected for a one year period between June 2012 and June 2013, were evaluated. Wind Speed data, collected for two different heights (20 m and 30 m) from a measurement station installed in Atilim University campus area (Ankara, Turkey), were recorded using a data logger as one minute average values. Yearly average hourly Wind Speed values for 20 m and 30 m heights were determined as 2.9859 m/s and 3.3216 m/s, respectively. Yearly and seasonal shape (k) and scale (c) parameter of Weibull Distribution for Wind Speed were calculated for each height using five different methods. Additionally, since the hub height of many Wind turbines is higher than these measurement heights, Weibull parameters were also calculated for 50 m height. Root mean square error values of Weibull Distribution functions for each height, derived using five different methods, show that a satisfactory representation of Wind data is achieved for all methods. Yearly and seasonal Wind power density values of the region were calculated using the best Weibull parameters for each case. As a conclusion, the highest Wind power density value was found to be in winter season while the lowest value was encountered in autumn season. Yearly Wind power densities were calculated as 39.955 (W/m2), 51.282 (W/m2) and 72.615 (W/m2) for 20 m, 30 m and 50 m height, respectively. The prevailing Wind direction was also determined as southeast for the region. It can be concluded that the Wind power density value at the region is considerable and can be exploited using small scale Wind turbines.
