Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor

Comment:

Major revision. More results and findings relative to model interpretability can be added to the paper showing its unique contributions and findings.

1. Please check the spelling of the words and delete unnecessary symbols, such as  regres-sion, opti-mal, radi-ation, present-ed, learn-ing...

2. I suggest that the abstract be refined by incorporating specific numerical data.

3. It is better to reorganize the introduction (now is too long), and ensemble methods can be mentioned in the literature review.

4. It is necessary to clarify the gaps between existing studies and your study, rather than simply listing and introducing the literature.

5. It is recommended to improve the clarity of the Fig.2.

6. Please draw a flowchart of your methodology, do not put the code directly.

7. To improve readability and avoid redundancy, the description of datasets should be condensed.

8. The methodology is, in my view, poorly described. It could be beneficial to better formalize the problem and how you intend to solve it.

9. Each models’ hyperparameter settings for the algorithms also need to be presented.

10. The conclusion usually needs to be combined with data to illustrate the most important contribution of the paper. And it is essential to critically discuss the limitations of the current study and identify avenues for future improvement at the conclusion of the paper.

 

Comments on the Quality of English Language

Minor editing of English language required

Author Response

 

1. Please check the spelling of the words and delete unnecessary symbols, such as  regres-sion, opti-mal, radi-ation, present-ed, learn-ing...

 

Response : it is OK

2. I suggest that the abstract be refined by incorporating specific numerical data

 

Response : Many thanks for this remark. We have done necessary change

 

3. It is better to reorganize the introduction (now is too long), and ensemble methods can be mentioned in the literature review.

Response : we are totally agree that the introduction too long. So, as you have proposed we have mentioned some methods in the next section

 

4. It is necessary to clarify the gaps between existing studies and your study, rather than simply listing and introducing the literature.

Response : it is updated in the literature review section

 

5. It is recommended to improve the clarity of the Fig.2.

 Response : we have done change as you recommended

6. Please draw a flowchart of your methodology, do not put the code directly.

It is a good remark

7. To improve readability and avoid redundancy, the description of datasets should be condensed.

Response : it is OK

 

8. The methodology is, in my view, poorly described. It could be beneficial to better formalize the problem and how you intend to solve it.

Response : we have enhanced this  section

 

9. Each models’ hyperparameter settings for the algorithms also need to be presented

 Response : it is OK

 

10. The conclusion usually needs to be combined with data to illustrate the most important contribution of the paper. And it is essential to critically discuss the limitations of the current study and identify avenues for future improvement at the conclusion of the paper.

Response : we have enhanced the conclusion

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Editor,

I am submitting my report on the manuscript entitled "Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor" which you asked me to review.

In this manuscript, Mohamed Khalifa Boutahir et al. present a new approach to improving solar radiation forecasting by using meta-learning techniques to optimize the weighting mechanism in the VotingRegressor ensemble. The authors have experimentally demonstrated the effectiveness of this approach in enhancing the accuracy and reliability of solar radiation forecasts. Indeed, the results show that the VotingRegressor with meta-learning techniques outperforms both the individual base estimators and the traditional VotingRegressor, achieving significantly lower prediction errors and higher coefficients of determination.

In my opinion, the article is well-structured, the analysis is sound, and the results are clear. This work could contribute significantly to the advancement of research in this field. I recommend the publication of this work without reservation.

Sincerely.

Author Response

I am submitting my report on the manuscript entitled "Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor" which you asked me to review.

In this manuscript, Mohamed Khalifa Boutahir et al. present a new approach to improving solar radiation forecasting by using meta-learning techniques to optimize the weighting mechanism in the VotingRegressor ensemble. The authors have experimentally demonstrated the effectiveness of this approach in enhancing the accuracy and reliability of solar radiation forecasts. Indeed, the results show that the VotingRegressor with meta-learning techniques outperforms both the individual base estimators and the traditional VotingRegressor, achieving significantly lower prediction errors and higher coefficients of determination.

In my opinion, the article is well-structured, the analysis is sound, and the results are clear. This work could contribute significantly to the advancement of research in this field. I recommend the publication of this work without reservation.

Sincerely.

Response : Many Thanks for your effort and time

Reviewer 3 Report

Comments and Suggestions for Authors

In this article, Boutahir et al. reported a Meta-learning method to improve solar radiation prediction. They reviewed some past progress and designed related experiments. However, I find the manuscript is organized in a confusing format, which needs to be improved. Here are some comments.

(1) In the first paragraph of the introduction, the research progress of solar cells should be briefly summarized. Therefore the authors may better understand why predicting solar radiation is an important topic. The following references can be cited to enhance the solar cell background: 

https://doi.org/10.1002/solr.202300479; https://doi.org/10.1038/s41578-022-00510-4.

(2) In section 2, they presented related works. It may be better to merge them into the introduction. 

(3) Compared to previous works, the novelty of this manuscript should be enhanced and specified in the introduction.

(4) In Figure 2, the differences between these two methods are not distinct. Can the authors add a reasonable explanation?

Author Response

In this article, Boutahir et al. reported a Meta-learning method to improve solar radiation prediction. They reviewed some past progress and designed related experiments. However, I find the manuscript is organized in a confusing format, which needs to be improved. Here are some comments.

 

(1) In the first paragraph of the introduction, the research progress of solar cells should be briefly summarized. Therefore the authors may better understand why predicting solar radiation is an important topic. The following references can be cited to enhance the solar cell background:

https://doi.org/10.1002/solr.202300479; https://doi.org/10.1038/s41578-022-00510-4.

Response : it is OK

 

(2) In section 2, they presented related works. It may be better to merge them into the introduction.

  Response : The introductions is too long (as reviewer 1 mentioned) So we have created a section  named the “Literature review”

 

(3) Compared to previous works, the novelty of this manuscript should be enhanced and specified in the introduction

Response : The comparison is done in the section 4

 

 

(4) In Figure 2, the differences between these two methods are not distinct. Can the authors add a reasonable explanation?

Response : it is OK

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

During the review process, I noticed that although the author has made some modifications to the manuscript, you have not sufficiently followed the previous suggestions of the reviewers, particularly in addressing the core issues, which remain inadequately resolved. These core issues are crucial for meeting the publication standards of this journal. 

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Dear Reviewer,

Thank you for your valuable feedback on our manuscript titled "Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor." We have carefully considered each of your comments and have made the necessary revisions to address them. Please find our point-by-point responses below:

Reviewer 1:

1. Abstract Refinement:

   • We have revised the abstract to include specific numerical data to summarize the key findings of our study. The updated abstract now provides clear information on the RMSE, MAE, and R² values achieved by our proposed model.

    

2. Clarify Research Gaps:

   • We have enhanced the literature review section to explicitly highlight the gaps in existing research and how our study addresses these gaps. This section now clearly outlines the limitations of previous studies and the contributions of our work.

    

3. Condense Dataset Description:

   • We have condensed the description of datasets in the methodology section to improve readability while retaining all essential information.

4. Formalize Methodology:

   • We have revised the methodology section to provide a more formal and detailed description of our approach. This includes a clear outline of our research design, data sources, procedures, and data analysis methods.

5. Present Hyperparameter Settings:

   • We have added a detailed description of the hyperparameter settings for each base estimator used in the VotingRegressor ensemble in the methodology section.

6. Enhance Conclusion:

   • We have strengthened the conclusion by summarizing the main findings with specific data, discussing the limitations of our study, and suggesting future research directions.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have addressed most of my concerns. Therefore, I recommend its publication.

Author Response

Dear Reviewer,

Thank you for your valuable feedback on our manuscript titled "Meta-Learning Guided Weight Optimization for Enhanced Solar Radiation Forecasting and Sustainable Energy Management with VotingRegressor".

We appreciate the positive feedback and recommendation for publication. We have further refined the manuscript to ensure clarity and comprehensiveness in addressing all review comments.

 

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

I have no further comments.