Explainable AI and optimized solar power generation forecasting model based on environmental conditions

Rizk M. Rizk-Allah; Lobna M. Abouelmagd; Ashraf Darwish; Vaclav Snasel; Aboul Ella Hassanien

doi:10.1371/journal.pone.0308002

Peer Review History

Original SubmissionMarch 12, 2024
8 Apr 2024 Decision Letter - Praveen Kumar Donta, Editor PONE-D-24-10041Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental ConditionsPLOS ONE Dear Dr. Hassanien, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by May 23 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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If you have no competing interests, please state "The authors have declared that no competing interests exist.", as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now This information should be included in your cover letter; we will change the online submission form on your behalf. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Partly Reviewer #3: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No Reviewer #3: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No Reviewer #3: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: No Reviewer #3: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The manuscript entitled "Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental Conditions”, presents a hybrid machine learning model, X-LSTM-EO and equilibrium optimizer (EO) to predict PV output power with high reliability. In this work, the authors proposed a framework for predicting power generation by using LSTM and component optimization of model's hyper-parameters by using EO component. The proposed work is worthy of investigation. The following are comments on this work to improve the readability of the article. 1)In the introduction, please correct the statement that which increases the power generation, and which decreases the power generation, “Temperature and solar irradiance changes can cause power generation to increase or decrease suddenly.” 2)Please give further details of “original model”, which is mentioned in the abstract. 3)Give a glimpse of software/methodology of the proposed model in the abstract. 4)Keywords are Vogue. Ideally, the keywords should be between 4-6. I suggest the following suitable keywords for this work. “1) Forecasting of solar power generation, ,2) Equilibrium Optimizer, 3) Long short-term memory (LSTM), 4) explainable artificial intelligence (XAI), 5) Local Interpretable and Model-independent Explanation (LIME), 6) Deployment of solar power plants. 5)Please provide the source data and code of the study. 6)A continuity is missing for starting of third paragraph of introduction. So, please merge it with the 2nd paragraph. 7)Introduction section could be improved by maintain a good flow of area of study, major literature and contributions. 8)How does the various MPPT techniques impacts the proposed study? Can the MPPT be investigated in this work? 9)Conclusion is needed to be improved by ignoring the repeated problem statement. 10)In the present study, only the input parameters for generation of power is considered. How could the study go if output loads considered? And further, is this work applied to constant power source generating systems such as small hydro-power systems, if yes, then pls give a future direction by considering the following latest references on constant speed-driven generating stations. https://doi.org/10.1016/j.renene.2022.06.051 https://doi.org/10.1016/j.rineng.2024.101761 Reviewer #2: The submitted manuscript titled 'Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental Conditions' presents a study of using optimized long short term memory algorithm to predict energy production from the sun using solar irradiance based on data from India available on Kaggle. However, the link provided at [36] did not lead to the dataset in question. The authors are advised to review all references, and update review of literature with recent publications from 2022-2024 regarding short-and long-term solar irradiance forecasting. While also subjecting the data to greater scrutiny. The novelty of the work as described in the manuscript is insufficient to merit further consideration. The authors must improve the state of the art of solar energy production forecasting using LSTM by a significant margin, and better highlight their contributions in the manuscript. The figures also need considerable effort to improve quality and readability, please avoid using screen shots or screen grabs and rely on professional graphics. The using of english language should also be reviewed. There are some instances where the meaning is not properly conveyed to the reader. While it doesn't pose a challenge to native speakers, it would be beneficial for the manuscript to undergo a thorough review of academic english language usage. Reviewer #3: The study developed a LSTM based model to forecast the solar power using explainable AI. The study is interesting and can be consider after strictly revision of following points. 1) Reduce the length of Abstract 2) As the LSTM used in the work, the literature of LSTM must be focused at maximum. study based on contrast models like GRU and BILSTM should also discussed in the literature. For reference, following studies should consider: https://www.tandfonline.com/doi/abs/10.1080/15435075.2022.2143272 https://link.springer.com/article/10.1007/s12145-023-01020-9 https://ajse.aiub.edu/index.php/ajse/article/view/212 3) Describe novelty and reason of development of such model only. 4) Section-1 have the literature & contributions based on studies. Why separate section -2 (related work) ? 5) Point wise conclusion can be provided. 6) Limitation of Work OR challenges ?? 7) Provide papers of last 5 years in literature. some are from year 2015,2017. Refer comment 2 8) Discussion must consider the topologies and complexities of the models. 9) Same for Comparision with different models. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: V. B Murali Krishna Reviewer #2: No Reviewer #3: Yes: Dr. Pardeep Singla ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0308002.r001
Revision 1
21 Apr 2024 Author Response Reviewer # 1 1)In the introduction, please correct the statement that which increases the power generation, and which decreases the power generation, “Temperature and solar irradiance changes can cause power generation to increase or decrease suddenly.” Response Thank you for your comment, it is revised and updated Page 2-5 2)Please give further details of “original model”, which is mentioned in the abstract. Response Thank you. The clarification was added to the abstract " These results improve the performance of the original model that acts without hyper-parameter optimization " Page1 3)Give a glimpse of software/methodology of the proposed model in the abstract. Response The clarification was added to the abstract. The proposed model is implemented utilizing Tensorflow and Keras within the Google Colab environment. Page1 4)Keywords are Vogue. Ideally, the keywords should be between 4-6. I suggest the following suitable keywords for this work. “1) Forecasting of solar power generation, ,2) Equilibrium Optimizer, 3) Long short-term memory (LSTM), 4) explainable artificial intelligence (XAI), 5) Local Interpretable and Model-independent Explanation (LIME), 6) Deployment of solar power plants. Response Thank you for your recommendation, it is revised and updated Page 2 5) Please provide the source data and code of the study. Response The source data is cited on page 10 via the hyperlink in reference number 36. Page 10 6)A continuity is missing for starting of third paragraph of introduction. So, please merge it with the 2nd paragraph. Response Thank you for your comment, it is revised and the introduction has been updated Page 2-5 7)Introduction section could be improved by maintain a good flow of area of study, major literature and contributions. Response Thank you for your comment, it is revised and the introduction has been updated Page 2-5 8)How does the various MPPT techniques impacts the proposed study? Can the MPPT be investigated in this work? Response The present study doesn't consider the MPPT techniques, it may be considered in the future work Page 32 9)Conclusion is needed to be improved by ignoring the repeated problem statement. Response Thank you for your comment, it is revised and the conclusion has been updated Page 31 10)In the present study, only the input parameters for generation of power is considered. How could the study go if output loads considered? And further, is this work applied to constant power source generating systems such as small hydro-power systems, if yes, then pls give a future direction by considering the following latest references on constant speed-driven generating stations. https://doi.org/10.1016/j.renene.2022.06.051 https://doi.org/10.1016/j.rineng.2024.101761 Response The proposed Work without electrical loads, and in the case of electrical loads, we will take the load distribution and apply the proposed model to it. Page 32 ======================= Reviewer # 2 he submitted manuscript titled 'Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental Conditions' presents a study of using optimized long short term memory algorithm to predict energy production from the sun using solar irradiance based on data from India available on Kaggle. However, the link provided at [36] did not lead to dataset in question. Response It works, this is the screen shot of the link site The authors are advised to review all references, and update review of literature with recent publications from 2022-2024 regarding short-and long-term solar irradiance forecasting. While also subjecting the data to greater scrutiny. Response Thank you for your comment, it is revised and the references has been updated Pages 32- 35 The novelty of the work as described in the manuscript is insufficient to merit further consideration. The authors must improve the state of the art of solar energy production forecasting using LSTM by a significant margin, and better highlight their contributions in the manuscript. Response thank you for your comment, but our novelty and the contributions in the paper are: • Deep learning models might not be as accurate because they use traditional optimization methods to find the best internal parameters. These techniques can get stuck in local minima, which leads to finding parameters that aren't as good as they could be. So, this paper solves these problems by adding the LSTM and the equilibrium optimization (EO) to the suggested model. This model is then used to show correctly how solar output power is related to external factors. • Applying the Equilibrium Optimizer (EO) algorithm for tuning the hyper-parameters the LSTM to enhance the performance of the forecasting, the performance was evaluated in this paper after the application of EO with LSTM. • Applying PSO optimizer for comparing its results with EO optimizer. • The utilization of LSTM for effective exploration of the search space without being trapped in local optima areas. • To understand the forecasting results. XAI's approach called LIME has been applied to explain the performance of the proposed deep learning model, the XAI explained the most important environmental condition that affects the model's forecasting results. • The propped X-LSTM-EO model proposes a common, accurate model that predicts well under many environmental scenarios. It mitigates PV power generation unpredictability and safely integrates large-scale PV power generation into micro grids, lowering operational costs and boosting efficiency and safety. The figures also need considerable effort to improve quality and readability, please avoid using screen shots or screen grabs and rely on professional graphics. Response Thank you for your comment, it is revised and updated The using of english language should also be reviewed. There are some instances where the meaning is not properly conveyed to the reader. While it doesn't pose a challenge to native speakers, it would be beneficial for the manuscript to undergo a thorough review of academic english language usage. Response Thank you for your comment, it is revised and updated ========================== Reviewer # 3 The study developed a LSTM based model to forecast the solar power using explainable AI. The study is interesting and can be consider after strictly revision of following points. 1) Reduce the length of Abstract Response Thank you for your comment, it is revised and the abstract has been updated Page 1 2) As the LSTM used in the work, the literature of LSTM must be focused at maximum. study based on contrast models like GRU and BILSTM should also discussed in the literature. For reference, following studies should consider: https://www.tandfonline.com/doi/abs/10.1080/15435075.2022.2143272 https://link.springer.com/article/10.1007/s12145-023-01020-9 https://ajse.aiub.edu/index.php/ajse/article/view/212 Response Thank you for your comment, it is revised and the introduction has been updated Page 2-5 3) Describe novelty and reason of development of such model only. Response Thank you for your comment, but our novelty and the contributions in the paper are: • Deep learning models might not be as accurate because they use traditional optimization methods to find the best internal parameters. These techniques can get stuck in local minima, which leads to finding parameters that aren't as good as they could be. So, this paper solves these problems by adding the LSTM and the equilibrium optimization (EO) to the suggested model. This model is then used to show correctly how solar output power is related to external factors. • Applying the Equilibrium Optimizer (EO) algorithm for tuning the hyper-parameters the LSTM to enhance the performance of the forecasting, the performance was evaluated in this paper after the application of EO with LSTM. • Applying PSO optimizer for comparing its results with EO optimizer. • The utilization of LSTM for effective exploration of the search space without being trapped in local optima areas. • To understand the forecasting results. XAI's approach called LIME has been applied to explain the performance of the proposed deep learning model, the XAI explained the most important environmental condition that affects the model's forecasting results. • The propped X-LSTM-EO model proposes a common, accurate model that predicts well under many environmental scenarios. It mitigates PV power generation unpredictability and safely integrates large-scale PV power generation into micro grids, lowering operational costs and boosting efficiency and safety. 4) Section-1 have the literature & contributions based on studies. Why separate section -2 (related work) ? Response Thank you for your comment, it is revised updated Page 2- 5 5) Point wise conclusion can be provided. Response Thank you for your comment, it is revised and the conclusion has been updated 6) Limitation of Work OR challenges?? Response Thank you for your valuable comment, The proposed model in this paper exhibits high prediction accuracy under various environmental conditions, demonstrating its universality and accuracy. It reduces uncertainty in PV power generation and safely integrates large-scale PV power generation into micro grids, reducing operating costs and improving efficiency and safety. It also introduces a new PV power generation forecast research direction: clustering data and noise reduction can reduce uncertainty. The research in this paper does not account for harsh weather conditions such as thunderstorms, sand, and dust. Response This part added to the conclusion section. Page 31 7) Provide papers of last 5 years in literature. some are from year 2015,2017. Refer comment 2 Response Thank you for your comment, it is revised updated 8) Discussion must consider the topologies and complexities of the models. Response Thank you for your comment. First, Table 9 displays the calculation of the training time for the three examined models throughout the training process. Which indicates the complexity of each one. Second, the section of the experiment, especially Table 7, page 21, explains the topology of our proposed model-based LSTM. Furthermore, the other techniques—linear regression and decision trees—do not resemble LSTM in their topology, hence it isn't very sensible to compare the topologies. Table 9 page 23 9) Same for Comparison with different models. Response Thank you for your comment, the Comparison exists in section 5.1 , table 13 Page 28 Attachments Attachment Submitted filename: Response to comment.pdf https://doi.org/10.1371/journal.pone.0308002.r002
30 Apr 2024 Decision Letter - Praveen Kumar Donta, Editor PONE-D-24-10041R1Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental ConditionsPLOS ONE Dear Dr. Hassanien, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Jun 14 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Praveen Kumar Donta, Ph.D. Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #3: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: (No Response) Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: (No Response) Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: I do appreciate the authors for a good revision. The following minor comments should be addressed for the final acceptance from my end. 1) Since the authors mention the MPPT is a future work, then they should give at least few reference for it. I suggest authors refer the dedicated review journals and cite appropriate publications. https://www.sciencedirect.com/journal/renewable-and-sustainable-energy-reviews 2) My earlier comment (comment 10) is partially addressed in the conclusion without proper citations in the conclusion for "distribution of electrical loads". 3) Should give the page numbers for the revised manuscript and line number as you mention in the Comments & Response sheet. 4) Please check the numbering for section and subsections properly and also provide the numbering for every subheadings. Wrong numbering is given for sections 4 and 5. 5) Provide a table for acronyms. Reviewer #3: Authors Incorporated all comments. Therefore, paper is recommended for publication. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: V. B Murali Krishna Reviewer #3: Yes: Dr. Pardeep Singla ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0308002.r003
Revision 2
6 May 2024 Author Response based on Environmental Conditions" Article Number: PONE-D-24-10041R1 Reviewer Comment Response Page Number Reviewer # 3 1) Since the authors mention the MPPT is a future work, then they should give at least few reference for it. I suggest authors refer the dedicated review journals and cite appropriate publications. https://www.sciencedirect.com/journal/renewable-and-sustainable-energy-reviews Thank you for your comment, it is revised. the text is updated and reference 49 is added Page 30. Line 3:6 2) My earlier comment (comment 10) is partially addressed in the conclusion without proper citations in the conclusion for "distribution of electrical loads". Thank you for your comment, it is revised. the text is updated and reference 50 is added Page 30 Lines 3:6 3) Should give the page numbers for the revised manuscript and line number as you mention in the Comments & Response sheet. Take into consideration 4) Please check the numbering for section and subsections properly and also provide the numbering for every subheadings. Wrong numbering is given for sections 4 and 5. Thank you for your comment, it is revised and updated 5) Provide a table for acronyms. List of Acronyms Table is added Page 2 Attachments Attachment Submitted filename: Response to comments 3-5.docx https://doi.org/10.1371/journal.pone.0308002.r004
10 Jun 2024 Decision Letter - Praveen Kumar Donta, Editor PONE-D-24-10041R2Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental ConditionsPLOS ONE Dear Dr. Hassanien, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Jul 25 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Praveen Kumar Donta, Ph.D. Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #4: (No Response) ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #4: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #4: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #4: No ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #4: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response) Reviewer #4: 1-The methodology section, particularly the description of the Equilibrium Optimizer (EO) algorithm and its implementation lacks clarity and detailed steps. The current narrative could benefit from a more structured presentation to facilitate comprehension. Including pseudocode or a flowchart would help in understanding the sequential steps of the algorithm. By visualizing the process, readers can better grasp the logic and flow of the EO algorithm. 2-More literature review is need for comparison. Add more studies and make comparative analysis with recent works, here are some examples: a.Bukhari SM, Moosavi SK, Zafar MH, Mansoor M, Mohyuddin H, Ullah SS, Alroobaea R, Sanfilippo F. Federated transfer learning with orchard-optimized Conv-SGRU: A novel approach to secure and accurate photovoltaic power forecasting. Renewable Energy Focus. 2024 Mar 1;48:100520. b.Abou Houran M, Bukhari SM, Zafar MH, Mansoor M, Chen W. COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. Applied Energy. 2023 Nov 1;349:121638. c.Khan UA, Khan NM, Zafar MH. Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0. Energy Conversion and Management: X. 2023 Oct 1;20:100486. 3-The data preparation phase mentions combining two datasets, but it lacks specifics on how missing values, outliers, and discrepancies between the datasets were handled. Effective data preprocessing is crucial for the reliability of the model, and a detailed account of these steps is essential. Providing detailed steps on data cleaning, handling missing values, and addressing any discrepancies between the two datasets before integration would ensure transparency and reproducibility of the results. 4-The explanation of evaluation metrics like R2, RMSE, MAE, COV, and EC lacks context and examples of how these metrics are calculated and interpreted. These metrics are pivotal in assessing the model’s performance, and a clear understanding of their computation and significance is necessary. Including a brief explanation with examples for each evaluation metric to illustrate how they are calculated and what they signify in the context of the model’s performance would enhance the manuscript's clarity and educational value. 5-While the paper compares the proposed model with Decision Tree and Linear Regression, it would benefit from a comprehensive comparative analysis with other advanced models, such as Gradient Boosting or other deep learning architectures. This expanded comparative analysis would provide a broader perspective on the proposed model’s performance and demonstrate its robustness and competitiveness against a wider array of contemporary methods. 6-Ensuring consistent use of terminology throughout the manuscript is crucial for clarity. For example, the term "Equilibrium Optimizer" should be uniformly referred to as EO throughout the text. 7-The manuscript contains typos and grammatical errors, such as "the" instead of "The". So, try to improve the English. 8-Some figures and tables are referenced in the text without proper context or description, making it difficult for readers to understand their relevance. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: Yes: V. B Murali Krishna Reviewer #4: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. 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Revision 3
12 Jul 2024 Author Response Response to Review Dear Prof. Editor in Chief, PLOS ONE Journal I would like to thank you very much for the time and effort working on the paper and for the valuable comments and revision which has improved the quality of this paper. In the next, I reply to the comments on the paper. All corrections are highlighted in the original text of the paper in yellow color. Manuscript Title: “Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental Conditions" Reviewer # 4 Comment 1-The methodology section, particularly the description of the Equilibrium Optimizer (EO) algorithm and its implementation lacks clarity and detailed steps. The current narrative could benefit from a more structured presentation to facilitate comprehension. Including pseudocode or a flowchart would help in understanding the sequential steps of the algorithm. By visualizing the process, readers can better grasp the logic and flow of the EO algorithm Response Thank you for your valuable comment, the algorithm has been revised and updated see Page 11, 12 Comment More literature review is need for comparison. Add more studies and make comparative analysis with recent works, here are some examples: a.Bukhari SM, Moosavi SK, Zafar MH, Mansoor M, Mohyuddin H, Ullah SS, Alroobaea R, Sanfilippo F. Federated transfer learning with orchard-optimized Conv-SGRU: A novel approach to secure and accurate photovoltaic power forecasting. Renewable Energy Focus. 2024 Mar 1;48:100520. b.Abou Houran M, Bukhari SM, Zafar MH, Mansoor M, Chen W. COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. Applied Energy. 2023 Nov 1;349:121638. c.Khan UA, Khan NM, Zafar MH. Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0. Energy Conversion and M-- Response Thank you for your comment. thee updates are doe as follows: - the literature review part is revised & updated - the table 1 is added, it contains "Related works for solar power prediction based on AI tools" - Reference 34,35 ad 36 are added to the paper Pages: 5, 6 7 and 36 Comment 3-The data preparation phase mentions combining two datasets, but it lacks specifics on how missing values, outliers, and discrepancies between the datasets were handled. Effective data preprocessing is crucial for the reliability of the model, and a detailed account of these steps is essential. Providing detailed steps on data cleaning, handling missing values, and addressing any discrepancies between the two datasets before integration would ensure transparency and reproducibility of the results. Response Thanks for your comment.Before data preparation, we do analysis for the data in section 3 which named " Dataset description and analysis", this step proved the normality of the data and it is clear in table 4 which preset the count , mean, std , min, ad max values of the dataset see Page 12 , 13 Comment 4-The explanation of evaluation metrics like R2, RMSE, MAE, COV, and EC lacks context and examples of how these metrics are calculated and interpreted. These metrics are pivotal in assessing the model’s performance, and a clear understanding of their computation and significance is necessary. Including a brief explanation with examples for each evaluation metric to illustrate how they are calculated and what they signify in the context of the model’s performance would enhance the manuscript's clarity and educational value. Response Thank you for comment. Section 4.3 called "Model evaluation" explains in details the evaluation metrics Page 22, 23 Comment 5-While the paper compares the proposed model with Decision Tree and Linear Regression, it would benefit from a comprehensive comparative analysis with other advanced models, such as Gradient Boosting or other deep learning architectures. This expanded comparative analysis would provide a broader perspective on the proposed model’s performance and demonstrate its robustness and competitiveness against a wider array of contemporary methods. Response Thank you for your valuable comment which proof the robustness and competitive of our proposed model. - We apply Gradient Boosting algorithm, the results added to table 9. the results of Gradient Boosting ensure the robustness of our work - The optimizer algorithm (EO)is used to get the optimal architecture of the deep learning (LSTM) Page 25 Comment 6-Ensuring consistent use of terminology throughout the manuscript is crucial for clarity. For example, the term "Equilibrium Optimizer" should be uniformly referred to as EO throughout the text. Response Thank you for your valuable comment, it is revised & updated All the paper Attachments Attachment Submitted filename: Response to Review 11-7-2024.docx https://doi.org/10.1371/journal.pone.0308002.r006
16 Jul 2024 Decision Letter - Upaka Rathnayake, Editor Explainable AI and Optimized Solar Power Generation Forecasting Model based on Environmental Conditions PONE-D-24-10041R3 Dear Authors, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. I would like to see some reference enhancements with the latest XAI work. Please consider this! Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Upaka Rathnayake, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #5: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #5: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #5: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #5: No ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #5: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #5: All the comments from the respective reviewers have been resolved In the abbreviation, LIME, should be model agnostic. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #5: No ******** https://doi.org/10.1371/journal.pone.0308002.r007
Formally Accepted
23 Jul 2024 Acceptance Letter - Upaka Rathnayake, Editor PONE-D-24-10041R3 PLOS ONE Dear Dr. Hassanien, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Professor Upaka Rathnayake Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0308002.r008

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