PONE-D-24-27898Energy Consumption Forecasting for Oil and Coal in China Based on Hybrid Deep LearningPLOS ONE

Dear Dr. Wu,

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.

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Ensure that words such as Novel, new study, first time are removed from the manuscript. Also clarify the meaning of hybrid deep learning methods, all figures should be clear. Authors should also explain the difference between hybrid and stacked ML models. Review for grammatical and spelling errors. 

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Please submit your revised manuscript by Sep 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.

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We look forward to receiving your revised manuscript.

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Academic Editor

PLOS ONE

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When submitting your revision, we need you to address these additional requirements.

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3. Thank you for stating the following financial disclosure: 

'Science and Technology Foundation of State Grid Corporation of China under grant 1400-202357341A-1-1-ZN (Identification of Energy Security Risks and Strategic Path Optimization Technology Research under Global Coal-Oil-Gas-Electricity Coupling in China)'

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.'' 

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'This work is supported by the Science and Technology Foundation of State Grid Corporation of China under grant 1400-202357341A-1-1-ZN (Identification of Energy Security Risks and Strategic Path Optimization Technology Research under Global Coal-Oil-Gas-Electricity Coupling in China).'

We note that you have provided funding information that is currently declared in your Funding Statement. However, 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 and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: 

'Science and Technology Foundation of State Grid Corporation of China under grant 1400-202357341A-1-1-ZN (Identification of Energy Security Risks and Strategic Path Optimization Technology Research under Global Coal-Oil-Gas-Electricity Coupling in China)'

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

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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: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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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

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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: No

Reviewer #2: No

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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 research presented in this paper is novel and yields significant results, but it requires substantial reshaping to be clear and impactful. In the abstract, the repeated mention of "generation of oil" should be corrected to "generation of coal" to accurately reflect the four targeted indicators. Additionally, more detail on what differentiates the five deep learning models would help readers understand the ensemble's diversity and benefits. Briefly mentioning the types of datasets used, such as the time span and data sources, would also provide clearer context for the experiments and enhance the abstract's informativeness.

The introduction could benefit from a revision of the first sentence for greater clarity, setting a better stage for the research. The data presentation throughout the paper has several issues that need addressing. The constant use of bullet points is distracting; a more narrative style would improve readability and flow. Table 1 is too dense with text, focuses on only one point, and thus is less effective. I do not understnad why that data presented is even in a table. Simplifying and summarizing the information will make it more reader-friendly. Table 2's placement in the paper is confusing and should be reconsidered for better logical flow. Moreover, tables 2 through 5 should be grouped together if they are related, and adding lines to these tables would improve readability. Each table should be accompanied by a general statement explaining its content and significance.

Figures 2 and 5 are blurry and hard to read, necessitating clearer visuals to effectively communicate the data. There is also insufficient explanation for each figure and table; more detailed descriptions rather than the current one-sentence explanations are needed. In Section 3.2.1, the bullet points are unnecessary and should be revised into a more cohesive narrative. Section 3.4 needs to be reworked entirely; the results discussed in the tables should be expanded upon, integrating the discussion into the overall implementation and implications of the work.

The summary requires several improvements. The repeated mention of "generation of oil" should be corrected to accurately reflect the four indicators: import of oil, generation of oil, import of coal, and generation of coal. Additionally, providing a brief explanation or example of what feature engineering involves would be beneficial, as not all readers may be familiar with the term. More detail on the specific deep learning models used in the ensemble would also give a fuller picture of the methodology and its advantages.

By addressing these points, the paper can better communicate its novel approach and significant findings, making it a clearer and more impactful contribution to the field of energy consumption forecasting. I cannot emphasize enough the importance of having more explanation of these tables and figures.

Reviewer #2: Review of Energy Consumption Forecasting for Oil and Coal in China Based on Hybrid Deep Learning

The authors have clearly demonstrated that the use of ensemble models is effective in addressing the limitations of single models in time series forecasting of heterogeneous indicators, including the superiority of using multi-feature data over univariate data in enhancing prediction accuracy. I recommend the acceptance of the paper for publication, subject to some revisions below. Overall, the paper needs some improvement/polishing of the grammar, especially the introduction. The authors should revise the grammar of the manuscript for better readability (I have highlighted JUST A FEW (as “Grammar” below – there is more.)

Introduction

• The second paragraph in this section should be re-written for better flow of thoughts. I find it hard to connect all the different statistical analysis methods and how one relates and/or build on the other. Each sentence in this section sounds disjointed from the preceding and/or proceeding sentence. Consider using words like “nevertheless”, “also”, “however”, “moreover”, “in addition” to connect the various statistical methods, and how they relate to each other.

• Paragraph 6. Rather than simply cite the other previous authors who have implemented ensemble modelling to energy related forecasting, the authors should elaborate more on these previous works (approach, outcomes, shortcoming). This will help build a case for the uniqueness of this article vs previous studies. For example, what makes your approach unique compared to other authors who have implemented ensemble modelling for energy related forecasting?

• Grammar: “Their consumption forecasting is crucial for China because it can not only provide a clear understanding of the future energy situation, but also help governments optimize and adjust its energy strategies to ensure energy security”, can be re-written as “Consumption forecasting is crucial in China as it not only provide a clear understanding of the future energy landscape but also helps the government to optimize and adjust strategies, thereby ensuring energy security”

• Grammar: Ensemble model combines a team of diverse models…Ensemble model combines a collection of diverse models

Section 2.3

• The authors have done an excellent job in describing the five forecasting models. However, they should state why these five models were specifically selected from a wide range of other possible numerous deep learning models (e.g. Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), Transformer, Seq2Seq (Sequence-to-Sequence) Model, DeepAR, TFT (Temporal Fusion Transformer), WaveNet, ConvLSTM, Multi-layer Perceptron (MLP) for Time Series, Deep State Space Models, DeepTCN (Deep Temporal Convolutional Networks), Variational Autoencoders (VAE) for Time Series, etc.). I am not suggesting including these models in your work but state the reason for the five models selected for this work. Are your selected five model superior to others? Does better at time series forecasting?

Section 3.2.3

• It is not clear what the authors mean by “years before 2017, 2018, 2019, 2020, and 2021”. Are they referring to years from 1999 to 2016? Or 2012, 2013, 2014, 2015, 2016?

Section 3.3

• Since the tables 6-9 contain a lot of data, the authors should provide more guidance to the readers on how to interpret the results in these tables. Either use short footnotes under the table to describe the data or embed some explanation in the text to aid readers’ understanding.

o For example, what does 0.99-1.0, 0.9-1.00,…etc., mean?

o Is the reader supposed to compare each column against “single” column?

o Do the % mean prediction error or accuracy? Are high % values good or bad?

o Any explanation for why the Average RSME (and Average MAPE) value decrease from left to right in Table 8 but increase from left to right in table 7. On the other hand, the values fluctuate in table 6 and 9. Please give adequate explanation

o “As shown in Tables 6 to 9, models with added features exhibit better prediction accuracy than the "Single" group”. By this statement, are you comparing the average values of each column (0.99-1.0, 0.9-1.00,…etc ) vs average of “single” column? This needs to be clarified, because some of the column values within each row are larger/smaller than the “single value”

o Overall, this section needs re-working

Section 3.4

• Line 8/9, generation of oil is repeated twice

• Correct “Important” in Table 12 and 14 headers to “Import”

• Ensure the % accuracies in lines 7/8 match the accuracies of each of the four indicators in Tables 11 to 14. See comment on conclusions section below for similar mistake.

• Grammar: “In conclusion, we conclude” is redundant. Perhaps you can update to “In conclusion, the identified significant feature…”

• What does “differencing” mean in the explanation of Arima (Table 10)

Section 3.5

• Is the “Experimental Details” in the first line of this section a subheading? Clarify or find a better way to integrate it into the rest of the sentences in that paragraph

• Only 4 ensemble models are listed in line 2 (instead of 5). The authors must have omitted XBOOST. Check and revise accordingly

• Check 101 neurons.we set the batch. There should be a space between “neurons” and “we”.

• The authors should specify in the table (refer readers to the table row/column) the one instance where the ensemble model performs worse than the LSTM model

• Grammar: “In the 100 comparison cases” is somewhat ambiguous. One possible adjustment will be “For each of the four indicators, 25 cases were run, giving a total of 100 cases, as show in Fig 15”

•

Section 3.6

• Grammar: Change beside to besides.

• Consider using the same vertical scale for Figure 5, 7 and any other figures which have similar data range in the vertical axis.

Section 4 (Conclusion)

• There is a mismatch % accuracy. For example, in table 11, the generation of oil accuracy improves by 2.2% not 2.12% stated in the conclusion. The authors should reconcile these % accuracies in the conclusion section to reflect the correct values in table 11

• “Generation of oil” is repeated twice in line 3 and 4 of your conclusion. The same error also in bullet point 2 of conclusion. This occurs in other places within the document. The authors should carefully go through the document to correct all of them.

• Grammar: We have evaluated our approach’s forecasting performance for… to “we evaluated the performance of our approach using four indictors…”

In General

• It is not clear what the authors mean by “ablation”, for example, “oil ablation”. Another word/synonym should be used instead to convey their meaning

• Consider using “Oil production” in place of “Oil generation”.

• Words like “competitors” is not appropriate for an academic journal publication. While it is good to benchmark your work against other authors/researchers, referring to them as competitors is not appropriate, as this is not a business endeavor. The authors should find better synonyms for “competitor” in the journal

• While it is good to compare the ensemble model against others using indices like loss/win, “prediction errors”, F-rank, etc., the authors should include an actual time series forecast. For example, take oil import indicator vs time, plot the real data vs ensemble model forecast, to show how their ensemble model performs vs real data.

• The use of prediction error and accuracy is somewhat ambiguous in the article. The authors should adopt one and stick to it

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Reviewer #1: Yes: Brooke Rogachuk

Reviewer #2: No

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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.

PONE-D-24-27898R1Energy Consumption Forecasting for Oil and Coal in China Based on Hybrid Deep LearningPLOS ONE

Dear Dr. Wu,

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.

==============================The Y axis of figure 6  (previously figure 7 in the original manuscript) as contained in the reviewer’s feedback comment was still not corrected. Should be corrected before publication!

Inconsistent citation style. 

==============================

Please submit your revised manuscript by Nov 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,

Jude Okolie, 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.

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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 #2: (No Response)

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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 #2: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

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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 #2: (No Response)

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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 #2: (No Response)

**********

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: The authors met all of my comments! I believe this is now a novel and interesting study and the paper reflects that!

Reviewer #2: Overall comment: The authors have implemented majority of the comments/concerns raised in the first manuscript. This article should be accepted for final publication, subject to the following minor revisions.

Table 14. The case of our approach loses the comparison is row of ‘2020’ in “Production of coal, not “Import of oil”

The authors have didn’t change the y-axis range in Figure 6 (previously figure 7 in the original manuscript) as contained in the reviewer’s feedback comment. Should be corrected before publication!

Mix of citation style in Introduction paragraph 2. E.g Tong et al(date?). and Rao et al(date?) is used together with numbered citations (e.g [1], [2], [3],…etc). If this is acceptable to the journal, fine. Otherwise, the authors should correct/unify these before final publication

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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: Brooke E. Rogachuk

Reviewer #2: No

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[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.

Energy Consumption Forecasting for Oil and Coal in China Based on Hybrid Deep Learning

PONE-D-24-27898R2

Dear Dr. Wu,

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,

Jude Okolie, Ph.D.

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 #1: All comments have been addressed

Reviewer #2: (No Response)

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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 #2: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

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 #2: (No Response)

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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 #2: (No Response)

**********

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: Figure 6 has been fixed, and everything looks fantastic now! This paper is not only well-structured but also presents some truly novel insights. Great work!

Reviewer #2: (No Response)

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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: Brooke Rogachuk

Reviewer #2: No

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