Modelling the Wind Speed Using Exponentiated Weibull Distribution: Case Study of Poprad-Tatry, Slovakia

Round 1

Reviewer 1 Report

The manuscript “Modelling the wind speed using exponentiated Weibull distribution: Case study of Poprad-Tatry, Slovakia” studied average hourly wind speed data from weather station Poprad, and a more flexible exponential Weibull (EW) distribution is used to simulate wind speed. The results of the goodness-of-fit criterion show that the east-west distribution fits seasonal and monthly wind speed data significantly better, especially around data peaks. The analysis of wind direction shows that the most prevalent direction is west (W), with an incidence of 34.99% and an average wind speed of 3.91 m/s, while the lowest incidence is north (N), with only 4.45% and an average wind speed of 1.99 m/s. It can be published in “Applied Sciences” after major revision. The concerns which should be considered by the authors are as follows:

1. It is suggested that the title of the “Introduction” part of the article should add a summary of the previous work, have a general introduction of the current development level of the issue and point out the highlights of the work of the article.

2. The “Abstract” states that "The EW distribution also proved to be a good model for highly right-skewed data". The author is requested to tell the advantages of EW distribution for the convenience of readers

3. The results and discussion part of the paper only describes the experimental results, without analyzing and discussing the generation of results. It is hoped that the author can add a discussion part to make the paper perfect.

4. In this paper, Weibull distribution and its main principles need to be introduced in detail so that readers can better understand the significance of this paper's research on wind speed distribution.

5. When inserting pictures such as Figure 1 and Figure 2 to introduce the location and surrounding conditions of Slovak meteorological stations, the size and appearance of the pictures should be adjusted so that you can draw or beautify them as much as possible.

6. What do parameters a and b represent in the probability distribution, and how does their meaning affect the wind speed distribution?

7. After estimating the parameters of a probability distribution model, what is the significance of the method used to evaluate the goodness of fit (GOF) of the model?

8. What software is used to obtain the wind speed data suitable for the probability distribution?

9. What is the significance of this paper to study the wind speed distribution in this region, and what are the main innovations of this paper?

10. Please explain why the author chooses the estimation method (maximum likelihood method).

11. Please explain the reasons why KS and AD tests are selected.

12. Please explain the reasons for choosing the meteorological station at Poprad-tatry Airport in the Prekov region of northern Slovakia as the data source.

13. The manuscript can refer to other research contents such as data modeling, wind energy, life cycle analysis, etc. Liu et al. analyzed the water footprint of the methanol production process in the life cycle based on the standard life cycle evaluation method (Science of the Total Environment, 2023, 856, 159129). Cui et al. studied and analyzed the life-cycle carbon footprint and water footprint of municipal sludge plasma gasification for hydrogen production and provided the carbon emission and water consumption under specific scenarios of municipal sludge plasma gasification for hydrogen production, providing support for its further development (Energy, 2022, 261, 125280). Shoaib et al., by fitting the measured wind speed data to the Weibull distribution function, conducted monthly, seasonal and annual analyses and estimated with the maximum likelihood method that the standard deviation value of the measured wind speed data distribution was in good agreement with the fitted Weibull distribution (Journal of Cleaner Production, 2019, 216, 346-360). Nair et al. proposed some transformations of quantile functions in information measurement. Some new properties of the Lemkuhler curve are derived using the quantile function (Journal of Informetrics, 2022, 16, 101266). Shahrbanoo et al. used wind speed data collected at nine sites in Ontario, Canada, to compare the performance of the proposed series with five distributed hybrid models already used. The results show that, according to the model selection criteria, the mixed model generally provides a better fit than the unimodal distribution (Renewable Energy, 2020, 196-211). Fatma et al. believe that in most cases, the IW distribution of MML estimates based on ML and parameters provides better modeling than the Weibull distribution based on corresponding estimates (Energy Convers. Manage., 2016, 234-240). Hicham et al. based their theoretical analysis on Weibull and Rayleigh stochastic models, and the results show that Weibull model is more accurate than Rayleigh model (Procedia Manufacturing, 2019, 786-793). M. El- Morshedy et al. used the maximum likelihood method to estimate the model parameters. The deviation and mean square error of the estimator are simulated (Math. Sci.,2020, 29-42). Zheng et al. make use of existing literature data to analyze the applicability of Falling Model and find that Falling Model has a wide range of application prospects (Renewable Energy, 2022, 91-99).

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

 

In this paper, the authors describes a statistical analysis of wind speed data collected from a meteorological station in northern Slovakia from 2005-2021. The analysis involved comparing the performance of the 2- and 3-parameter Weibull distribution to the exponentiated Weibull (EW) distribution in fitting the data. Results showed that the EW distribution provided a significantly better fit to the data, especially around the peaks and for highly right-skewed data. The EW distribution was recommended as a flexible distribution for modeling data with extreme or outlier right-tail wind speeds. The analysis also revealed that the most prevailing wind direction was west (W), with the lowest occurrence rate and mean wind speed being observed for the northern (N) direction. The topic is consistent with Applied Sciences Journal. However, the authors need to answer the following questions before we accept this work.

Introduction part:
1. What are the factors that affect wind speed, and why is wind speed modeling important?

2. What is the 2-parameter Weibull distribution, and why is it commonly used for modeling wind speed?

3. What are the limitations of the 2-parameter Weibull distribution, and what are some alternative probability distributions that have been used for wind speed modeling?

4. What is the exponentiated Weibull distribution, and why is it being studied as an alternative to the 2-parameter and 3-parameter Weibull distributions?

5. How were the goodness-of-fit tests and information criteria used to assess the suitability of the probability distributions for modeling wind speed in the Poprad-Tatry airport area, and what were the results of the analysis?

Results and Discussion:

1.      What are the two distributions compared to the EW distribution in terms of fitting wind speed data?

2.      Which distribution performs better in February and March?

3.       What are the results of the GOF tests and model selection criteria for the three distributions?

4.      What criteria is given more weight when choosing the most appropriate distribution for a given month?

5.      Which distribution is more suitable for modelling wind speed in the studied location and for all seasons according to the criteria?

6.      What advantage does the EW distribution have over the W2 and W3 distribution in modelling wind speed data?

 

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Please see the attached file for comments and suggestions. 

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

All comments have been revised. I suggest that it be published on Applied Sciences.

 

Reviewer 3 Report

The authors almost addressed all my concerns/comments comprehensively.

The paper has enough contribution to be published.

 

The paper can be accepted in its current form.