Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction

Round 1

Reviewer 1 Report

This paper introduces a novel approach to generating extended solar power time- 10

series data by leveraging Time-series Generative Adversarial Networks (TimeGAN) in conjunction with adjustments based on sunrise-sunset times. The work is interesting, however there are corrections required to enhance the quality of the work. The comments are listed.

1. Page 4 line 142: “Historical solar power time- series data,x”, Does the historical data take into account meteorological conditions other than solar energy and how accurate is the data? Please add it in the text.

2. Page 9 line 325: It is suggested to add a column of analytical calculation of error values in Table 1, and add a part of analysis in the work.

3. Part 3.1: In addition to the number of iterations, how are the remaining hyperparameters of the model set before training? please add it in the text.

4. Figure 4: How the typical data is selected and why only a small part of the data is amplified, please add a part of explanation in the article.

5. There are some symbols and formatting errors in the paper, such as the sentence "Therefore, it may cause the generated data to differ significantly from the historical data." in Page 2 line 85, which is suggested to be changed after reviewing the whole work.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

The authors have worked on "Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise-Sunset Time Correction". The work seems interesting and can be considered after a few modifications.

 

Abstract: The section needs to be modified quantitatively.

 

Introduction: The section is well written however the novelty of the manuscript needs to be added along with recent literature.

 

How does the current article differ from "A Multi-step ahead photovoltaic power forecasting model based on TimeGAN, Soft DTW-based K-medoids clustering, and a CNN-GRU hybrid neural network?”

 

Author need to add table of comparison with other researcher method. 

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Outlined below are the key areas that need to be addressed:

The introduction lacks depth and could benefit from providing a broader overview of the current challenges in generating long-term solar power time-series data and how existing methods fall short. Additionally, the references in the first 12 lines of the introduction are limited. I recommend discussing more about the state-of-the-art in this field and providing a more comprehensive literature review.

Several statements and equations in the manuscript lack proper referencing. Specifically, lines 56 to 59 and lines 61 to 66 require references to justify the claims made. It is essential to acknowledge the sources for the equations presented in the manuscript.

The manuscript does not clearly explain the reason behind selecting the specific case study presented in Section 3. Please provide a justification for choosing this particular case study and how it aligns with the research objectives.

The manuscript would benefit from a detailed discussion on the limitations and potential sources of error in the generated data. Specifically, address scenarios or factors in which the TimeGAN-based approach may not perform effectively and elaborate on how the proposed method handles outliers or extreme weather conditions that may significantly impact solar power generation.

Strengthen the manuscript by discussing the potential applicability of the proposed method to different geographical regions, solar power station configurations, and data sources. It is important to highlight any known limitations or challenges when scaling up the method to larger solar power systems.

The manuscript does not explicitly address the computational efficiency of the proposed method. I recommend providing a discussion on the computational resources required for training the TimeGAN model and generating the long-term solar power time-series data.

Author Response

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Author Response File: Author Response.pdf

Reviewer 4 Report

The paper shows interesting results. That sounds good to be published in the paper, but it needs revisions to improve the quality based on the comments below:

1) Abstract should be revised to add the basic knowledge, novelty and contribution, especially outstanding results.

2) In Introduction, many paragraphs were included. Authors should revise it to summarize in four ones, including: 1) basic knowledge and motivation; 2) Literature review; 3) Summarize shortcomings and contributions of previous studies; 4) Study, novelty and contribution of the paper.

3) Section 2 is very poor. Authors should reduce text and add more equations and pictures for clarifying the problem. Presentation of the section is poor too. Please put symbols in Nomenclature and please refer to good papers to have good organization for the paper.

4) Authors must show references for equations and data.

5) Please explain numbers in Eqs. 13-16, 18.

6) What is h in the statement: "The angle of h is -0.833°". Why it has the angle of -0.833 ?

7) In Eq. (22), what happen if j=k?

8) Check Eq. (23), it seems to have typos.

9) Check Table 1. I see "1st, 2st, .., 10st". What are they?, check English please.

10) Format Figure 6, it is not clear and It is larger than template.

11) Figure 7 needs improvement. width of curves need to be higher, numbers need to be larger.

12) Please format Table 3 and explain values in this.

13) Check typos and English, such as "IEEE39 bus system digram".

14) Why is time in Figure A2 set to 45. Please change green curve to black curve and increase the width. Similarly, Figure A3 need to check and revise.

15) The conclusion is weak. A more detailed conclusion is required. The most important obtained results should be briefly and clearly mentioned through the support of numerical data in the conclusion. What were the most sounding quantifiable findings of this study? Please point shortcomings and solutions to these together with future work.

 

 

English needs revisions.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Thank you for your answers. Good luck.

Reviewer 4 Report

It is accepted for publication in the journal 

English need revision