Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition

Shaokun Liang; Tao Deng; Anna Huang; Ningxian Liu; Xuchu Jiang

doi:10.1371/journal.pone.0277085

Peer Review History

Original SubmissionJanuary 21, 2022
28 Apr 2022 Decision Letter - Le Hoang Son, Editor PONE-D-22-02103Energy consumption prediction using GRU-MMattention-LightGBM model with features of Prophet decompositionPLOS ONE Dear Dr. Jiang, 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 12 2022 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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Review Comments to the Author Reviewer #1**: The paper proposes a hybrid machine learning model, GRU-MMattention-LightGBM, with feature selection based on prophet decomposition. 1. The contributions are not presented clearly in Section 1. After the literature survey, the authors should explain technology gaps and the paper's novelty. 2. The authors stated that the proposed method has a strong anti-noise ability, which can reduce the impact of mutation points. This capability needs to be verified and discussed in the case study. 3. The font in Figures 5 and 6 is too small to read. 4. There are so many typos and grammatical errors as follows: Line 53: Jianet al. Line 85: multiple spaces Line 88: T The Line: 312: multiple spaces 5. Usually, the conjunction "And" does not come as the first sentence word. [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.0277085.r001
Revision 1
16 May 2022 Author Response Dear Editors and Reviewers: Thank you for your letter and for the reviewer's comments concerning our manuscript entitled “Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition” (PLOS ONE). These comments are all valuable and very helpful for revising and improving our paper and have important guiding significance to our research. We have studied the comments carefully and have made corrections that we hope will meet with approval. The main corrections in the paper and the responses to the reviewer’s comments are as follows. Reviewer #1: The authors well have handled the reviewer's comment. I have additional comments for this revision as follows; Comment 1: 1. The contributions are not presented clearly in Section 1. After the literature survey, the authors should explain technology gaps and the paper's novelty. Thank you for your comments. We reorganized the literature, pointed out the shortcomings of current techniques and supplemented the novelty of our proposed methods. New content is added on line 69： However, RNN-based models are sensitive to different random weight initializations and thus prone to overfitting. Therefore, the single network structure lacks predictive stability and generalization ability. New content is added on line 79： Without sufficient subsampling，randomized trees can lead to overfitting, according to Speiser J et al.[22] New content is added on line 83： However, their deep neural network model is also sensitive to different random weight initializations, which is very unstable and does not combine with machine learning methods. New content is added on line 86： However, GRU is used for feature extraction in their work, the periodicity of energy consumption data is not well captured, and GRU-attention has higher prediction accuracy than GRU. New content is added on line 92： This method only uses an attention structure, and has a poor ability to learn the periodic characteristics of power consumption, and the accuracy is not as good as that of GRU-attention. New content is added on line 95： LightGBM, a single machine learning model, has strong predictive ability in general time series, but its accuracy in predicting periodic time series is still inadequate. Deep learning methods, such as LSTM, GRU and other RNN-based models, rely heavily on initialization parameter selection, and the model stability and accuracy is not good enough. A single GRU or a single attention is not as good at predicting periodic time series as a combination of the two. Current research cannot solve the above three key problems simultaneously. Comment 2: 2. The authors stated that the proposed method has a strong anti-noise ability, which can reduce the impact of mutation points. This capability needs to be verified and discussed in the case study. Response: Thank you for your comments. Noise error refers to the difference between the label value of the data and the real value, which belongs to the measurement error. The strong anti-noise capability in this paper refers to the strong anti-interference capability of the model against the noise of the training set. Specifically, if noise appears in the training data set and the model learns the data contaminated by noise, the prediction result is still not much different from the data without noise, then we can conclude that the model has a good anti-noise ability. Based on the above ideas, we supplement a new noise experiment in Section 3.6. We selected the energy consumption data of PJM regional power supply company, different from those in the previous areas. The power consumption data from 0:00 on January 1, 2017 to 23:00 on August 3, 2018 were selected, with a frequency of 1 hour, and a total of 11380 samples. After data cleaning, we added Noise to 10% of the data in the training set, Noise~N(0,1000), where 1000 is the standard deviation of the power consumption data itself. Finally, the model is retrained and metrics are calculated on the test set. Repeat the experiment 10 times and record the metrics for each time. After the t-test of the 10 experimental results, if metrics did not change significantly, the GRU-MMatten-LGB method could be considered to have a strong anti-noise ability. Table 6 shows the MAPE of 4 different models on the test set after randomly adding normal distribution noise to 10% of the training set. Since the premise of t test is that the population of the MAPE in the nosie experiment follows normal distribution, we first adopt Shapiro-Wilk test on the 10 results of each model to judge whether they follow normal distribution. H_0:Distribution of MAPE on noise experient ~N(u,σ^2) H_1:Distribution of MAPE on noise experient doesn^' t follow N(u,σ^2) It can be seen from Table 7 that the above p-values are all greater than 0.1, so there is no reason to reject the null hypothesis. Therefore, we conclude that the distribution of MAPE in noise experiment conforms to normal distribution. Then we can check whether the MAPE mean of the noise experiment is smaller than the given population mean, which is denoted as MAPE_normal H_0:Mean_noise≤MAPE_normal H_1:Mean_noise>MAPE_normal Mean_noise represents the mean of the MAPE on a certain model in 10 noise experiments, and the data are shown in Table 6. Mean_normal represents the MAPE of a certain model without noise, and the data are shown in Table 5. The results of the t-test would be shown in Table 8. According to the results of Table 6 and Table 8, p-vlaue of t-test in GRU noise experiment is less than 0.1. The mean of MAPE on GRU model in 10 noise experiments is 0.03327, which can be considered to be significantly higher than 0.028152 when noise is not added. Therefore, we can conclude that GRU model has poor anti-noise ability. Similarly, the p-value of the t-test in GRU-MMattention noise experiment is less than 0.1, we can also draw the conclusion that GRU-MMattention is poor in anti-noise ability. However, LightGBM method in the noise experiment, its p-vlaue of t-test is more than 0.1. The mean of MAPE on LightGBM in 10 noise experiments is 0.02720, which cannot be judged to be significantly greater than 0.026851 without noise. It can be considered that the impact of noise on the prediction results of LightGBM model is not significant, and the LightGBM method has a strong anti-noise ability. Similarly, the p-vlaue of t-test on GRU-MMatten-LGB is also more than 0.1.It can be concluded that GRU-MMatten-LGB has a strong anti-noise ability. Comment 3: 3. The font in Figures 5 and 6 is too small to read. Response: Thank you for your comments. The Figures 5 and 6 have been revised as follows. Comment 4: 4. There are so many typos and grammatical errors as follows: Line 53: Jianet al. Line 85: multiple spaces Line 88: T The Line: 312: multiple spaces Response: Thank you for your comments. We are very sorry for our careless mistake and it was rectified at Line 53, 85, 88, 312. At the same time, we checked the full text. Comment 5: 5. Usually, the conjunction "And" does not come as the first sentence word. Response: We appreciate it very much for this good suggestion, and we have done it according to your ideas. We revised the abstract and Section 2.2 with this problem. Responses to the Editor: The modified manuscript has been improved and polished by professional services. All modifications have been marked in RED, and a point-by-point reply has been completed. We tried our best to improve the manuscript and made some changes in the manuscript. Thank you very much for your thorough review and valuable advice. Your comments and suggestions have made a great contribution to the improvement of this paper's quality. Thank you and best regards. Xuchu Jiang xuchujiang@zuel.edu.cn School of Statistics and Mathematics Zhongnan University of Economics and Law Attachments Attachment Submitted filename: Response to the comments_5.0.docx https://doi.org/10.1371/journal.pone.0277085.r002
20 Oct 2022 Decision Letter - Shuo-Yan Chou, Editor Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition PONE-D-22-02103R1 Dear Dr. Jiang, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, 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, Shuo-Yan Chou 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 ******** 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 ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: 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 ****** 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 ****** 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 have adequately addressed all the comments raised by this reviewer. No more comments. ****** 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: No ******** https://doi.org/10.1371/journal.pone.0277085.r003
Formally Accepted
21 Nov 2022 Acceptance Letter - Shuo-Yan Chou, Editor PONE-D-22-02103R1 Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition Dear Dr. Jiang: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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 plosone@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 Shuo-Yan Chou Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0277085.r004

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