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Article
Peer-Review Record

The Mixture of Probability Distribution Functions for Wind and Photovoltaic Power Systems Using a Metaheuristic Method

Processes 2022, 10(11), 2446; https://doi.org/10.3390/pr10112446
by Amr Khaled Khamees 1, Almoataz Y. Abdelaziz 2, Makram R. Eskaros 1, Mahmoud A. Attia 3,* and Ahmed O. Badr 3
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 3:
Reviewer 4: Anonymous
Processes 2022, 10(11), 2446; https://doi.org/10.3390/pr10112446
Submission received: 26 October 2022 / Revised: 6 November 2022 / Accepted: 12 November 2022 / Published: 18 November 2022
(This article belongs to the Special Issue Sustainable Microgrid Systems: Technologies, Applications and Trends)

Round 1

Reviewer 1 Report

1. The Abstract is presented very nicely. The incorporation of the quantitative results at the end would add value to it.

2. The research gap is missing from the introduction section.

3. Presenting the methodology with an illustrative flowchart or diagram is advisable.

4. Sections 2, 3, 4, 5, and 6 can be merged as a single section titled 'methodology' or 'mathematical modeling'.

5. A quantitative Improvement in wind speed and solar irradiance prediction should also be discussed along with the presently given RMSE values. 

 

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

Some minor typos were found. A complete revision of the English language should take care of it.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

Please see my comments in the pdf file. 

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.docx

Reviewer 4 Report

Based on four typical probability distribution functions, this paper aims to improve the sufficiency and accuracy of the modelling to predict the distribution curve. This paper combined two or three from the four original functions and then compared the modelling results. My technical concerns are:

1.     The modelling results and the fitting parameters in this pater were based on the real historical data using Mayfly algorithm. However, there are no details on how the multiple parameters were calculated by Mayfly algorithm and how the real data were used.

2.     In every model presented in this paper, more details should be given to explain the meaning of the multiple parameters.

3.     Authors combined two or three of the four original models with a different scale and weight to each distribution functions. How the scale and weight were calculated?

4.   The modelling results have shown that three-component mixture distributions have the best statistical error calculations when compared to the original and two-component distributions. However, there are four combinations from different types of the original functions, and much more from mixture of same type and different type. How was the best combination was optimized?

5.    How many real data in total was collected and used in the statistic analysis in this paper?

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have incorporated the suggestions satisfactorily. 

The manuscript is recommended for Acceptance

Reviewer 3 Report

The authors addressed most of my concerns from the first review. 

 

Reviewer 4 Report

 

 

 

 

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