https://orcid.org/0000-0002-0002-9386
Peer Review History
| Original SubmissionJuly 24, 2024 |
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PONE-D-24-30954Novel Deep Neural Network Architecture Fusion to Simultaneously Predict Short-Term and Long-Term Energy ConsumptionPLOS ONE Dear Dr. Syafrudin, 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 Oct 28 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:
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, Jinran Wu, PhD Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, all author-generated code must be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. 3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. 4. Thank you for stating the following financial disclosure: “This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.1711160571, MLOps Platform for Machine learning pipeline automation.)” Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf. 5. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript. 6. When completing the data availability statement of the submission form, you indicated that you will make your data available on acceptance. We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process. 7. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 7 in your text; if accepted, production will need this reference to link the reader to the Table. Additional Editor Comments (if provided): Please carefully revise the manuscript with two referees' comments. [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: Yes ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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: Yes Reviewer #2: 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: 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 addresses an important issue in energy consumption prediction by proposing a novel hybrid model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) architectures to predict both short-term and long-term residential energy consumption. While the approach is interesting and potentially valuable, there are several areas where the manuscript could be improved. 1. The paper claims to present a novel hybrid model for predicting energy consumption. However, the innovation appears to be relatively incremental given the existing literature on CNN, LSTM, and Bi-LSTM architectures. 2. The rationale for combining short-term and long-term predictions in a single model is not sufficiently justified. Why is it necessary to predict both simultaneously? What specific advantages does this approach offer over using separate models for short-term and long-term predictions? 3. The manuscript does not provide adequate details on how the data for different prediction horizons were obtained. Are these data sets collected from the same source? How do the authors handle the differences in data frequency and resolution? 4. The manuscript lacks sufficient data visualization to support its claims. Data visualizations such as time series plots, correlation matrices, or feature importance rankings would provide valuable insights into the data and the model's performance. 5. While the manuscript reports performance metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE), it does not provide a comprehensive comparison with baseline models or other state-of-the-art methods. 6. The evaluation section would benefit from additional details about the experimental setup, including the size of the training and testing datasets, the hyperparameters used for the models, and the computational resources required. Multiple experiments are also necessary. 7. Ensure that all acronyms and technical terms are clearly defined when first introduced to readers. Reviewer #2: The manuscript presents a hybrid deep learning model integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for forecasting energy consumption. I have several suggestions and concerns that need to be addressed before the manuscript can be considered for publication. 1. Comparison with Decomposition Algorithms: The authors mention the use of Empirical Mode Decomposition (EEMD) in existing literature for daily energy consumption prediction. It is suggested that the manuscript would benefit from the inclusion of comparative experiments that analyze the performance of the proposed model against models incorporating decomposition algorithms like EMD, EEMD and CEEMDAN. 2. Theoretical Justification: The manuscript claims that the proposed model can effectively handle both short-term and long-term energy consumption predictions. However, a theoretical explanation is lacking on how the model achieves this dual capability. The authors should elaborate on the theoretical aspects and mechanisms that allow the model to address the complexities of short-term and long-term predictions simultaneously. 3. Rationale for Model Parameter Selection: Please provide the basis for the hyperparameters chosen for the models. 4. Ablation Study: To better understand the contribution of each component of the proposed model, an ablation study could be beneficial. The authors are encouraged to compare the performance of the model with different configurations, such as CNN+BiLSTM, CNN+LSTM, and BiLSTM+LSTM. 5. Figure 3 Clarifications and Enhancements: The visualization in Figure 3 is not clear and uses a mix of bar charts and line graphs without a clear rationale. The authors should reconsider the design of Figure 3 to ensure that it effectively communicates the results. Additionally, the figure should be redrawn with higher resolution to improve clarity. 6. Consistency in Comparative Models: There is an inconsistency in the models compared across Table 3, Table 4, and Table 5. The authors should provide a rationale for these differences in the comparison 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: No Reviewer #2: 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. 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. |
| Revision 1 |
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PONE-D-24-30954R1Novel Deep Neural Network Architecture Fusion to Simultaneously Predict Short-Term and Long-Term Energy ConsumptionPLOS ONE Dear Dr. Syafrudin, 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 Dec 26 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:
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, Jinran Wu, PhD Academic Editor PLOS ONE [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: (No Response) 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: Partly ********** 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: Yes 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: Yes 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: 1. The abstract is overly verbose, with much of it spent summarizing previous literature rather than succinctly presenting the motivation and unique contributions of the research. The novelty is not clearly convincing, making it difficult for readers to grasp the specific advancements introduced. 2. The paper lacks logical coherence; for example, the dataset is introduced within the methodology section, which is misplaced. Additionally, the research motivation is presented only after the literature review and formulation of the research question, creating a confusing sequence. Reorganizing the content would improve clarity. 3. The order of the three models (CNN, LSTM, and Bi-LSTM) in the hybrid model lacks justification. An explanation for this specific order, as well as a consideration of alternative arrangements, would strengthen the methodology. 4. The figures are low quality, with inconsistent font sizes, and they lack essential comparative elements, such as prediction comparison curves, which would be more informative than the current error convergence graphs. Adding these would improve the visual relevance and readability. 5. Lastly, the English language quality is poor, with significant grammatical and syntactical issues throughout. Professional editing or proofreading is strongly recommended to enhance the readability and professionalism of the manuscript. Reviewer #3: Dear Editor, Thank you for the opportunity to review this article. Overall, I believe the topic discussed in this paper is very meaningful, and the structure is fairly clear. The model proposed by the authors effectively addresses both long-term and short-term energy consumption issues, which holds significant value for time-series forecasting problems. Below are some of my personal comments for reference: 1.The evaluation results are confusing: It appears that different models were used when testing with data of varying levels, which is somewhat perplexing. Typically, it is expected to use the same set of models for comparison. 2.The model comparison is not comprehensive enough: The proposed model combines CNN, LSTM, and Bi-LSTM. As part of the ablation study, adding evaluations of Bi-LSTM would further validate the significance of each component in the proposed model architecture. Additionally, the attention mechanism is widely used in time series prediction tasks, and readers would be interested in a performance comparison between the proposed method and attention-based models. 3.Flaws in the design of the comparative experiment: the authors considered that their proposed model performed well in both long-term and short-term forecasting. However, the experimental design appeared to have only adjusted the time granularity without changing the sequence length, which could not intuitively demonstrate the model’s effectiveness on both long and short sequences. 4.The description of the ablation experiments is not sufficiently clear: the paper used data at different levels, but it was not explicitly stated in the ablation experiment whether the data used corresponds to a specific level or was an aggregated result across different levels. Additionally, including the baseline results in Table 9 would facilitate easier comparison. 5.Limited discussion on computational complexity: The authors didn’t discuss the computational complexity of their proposed model, which was crucial for evaluating its scalability and practical use in real-world applications. 6.The results in the charts are not sufficiently clear: the font in the images is too small. What does the black line in Figure 5 represent? Why are only some features labeled? Recommendations: 1.In evaluations based on data of different levels, try to use only the same type of models. Include the performance of the baseline model Bi-LSTM as well as attention-based models. 2.Adding an evaluation of the model's predictive performance across different sequence lengths could better demonstrate the robustness of the proposed model. 3.Refine the description of the ablation experiments and highlight the conclusion that each component of the model’s three architectures (CNN, LSTM, and Bi-LSTM) is meaningful. 4.Add an evaluation of the model's operational efficiency or computational complexity. 5.Enlarge the font in the image results, and provide explanations for any special symbols. 6.Notably, the performance of the model proposed by the authors, especially in terms of MSE, was hundreds of times lower than that of other common time series prediction models. If there are no issues, such as recording errors, in the experimental results, this would represent a significant breakthrough. It is recommended that the authors submit their code to verify the authenticity of the experimental results. In summary, the ideas in this paper are highly creative, and the structure is relatively clear. If the authors agree with and address the issues mentioned above, I recommend it for publication. ********** |
| Revision 2 |
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Novel Deep Neural Network Architecture Fusion to Simultaneously Predict Short-Term and Long-Term Energy Consumption PONE-D-24-30954R2 Dear Dr. Muhammad Syafrudin, 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. 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, Jinran Wu, PhD Academic Editor PLOS ONE |
| Formally Accepted |
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PONE-D-24-30954R2 PLOS ONE Dear Dr. Syafrudin, 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 Dr. Jinran Wu Academic Editor PLOS ONE |
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