All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
All the concerns have been addressed well in the revised version and all the reviewers satisfy the revision. The authors have clearly explained the requested 4 standards in the editor's letter. Since no further comments are received, I recommend accepting this manuscript as it is based on solid contributions.
[# PeerJ Staff Note - this decision was reviewed and approved by Marieke Huisman, a PeerJ Section Editor covering this Section #
This section has been fully revised in accordance with my previous comments
This part has been fully revised in accordance with my previous comments
This section has been fully revised aacording to my suggestion.
This paper has been fully revised in accordance with my previous opinions, and I suggest acceptance .
Questions are answered, no more comments
Questions are answered, no more comments
Questions are answered, no more comments
Questions are answered, no more comments
The paper is well written with publishable contributions. However, some minor issues should be taken into account before accepting it. Basically, the study investigates two methods GRA and K-means. The authors have not clearly highlighted the advantages and disadvantages of these two methods and why the study is around two methods. Generally speaking, we only focus on the most advantage method rather than comparing various methods, unless the method has its own benefit. The presentation should also be improved, such as the symbol and terminology explanations. For example, what is PSO short for? The authors should further check all the typos and grammar problem in the revision.
More importantly, we notice a paper related to the contents of this submission: Aranha, C., Camacho Villalón, C.L., Campelo, F. et al. Metaphor-based metaheuristics, a call for action: the elephant in the room. Swarm Intell 16, 1–6 (2022). https://doi.org/10.1007/s11721-021-00202-9. In which, the following 4 standards have been mentioned. Please address all the following points in the revision.
> (i) present their method using the normal, standard optimization terminology;
> (ii) show that the new method brings useful and novel concepts to the field;
> (iii) motivate the use of the metaphor on a sound, scientific basis;
> (iv) present a fair comparison with other state-of-the-art methods using state-of-the-art practices for benchmarking algorithms.
In summary, a major correction is needed to improve the quality of the manuscript.
(1) Figure 13 is not clear enough, it is recommended to improve the clarity.
(2) Figure 14 has the same problem with before.
(1) The study uses GRA and K-means clustering to select two similar days. What is the advantage of two similar days selection over one similar day selection? No description is seen.
(2) What preprocessing was performed on the original data during the example simulation? No description is seen.
(3) What is the quantitative basis for the wind direction, whether a working day or a holiday and the weather when the article conducts the Pearson correlation coefficient analysis?
(1) When using LSTM neural network for prediction, is the input data selected after two similar day selections? This has an impact on the validity analysis of the experimental results.
(1) In the process of SVM training, what is the basis for selecting the SVM kernel function? No description is seen.
(2) How are insensitive loss factors valued? How does the loss factor affect the accuracy of power load forecasting?
This paper proposed a short-term load forecasting method, which is based on the gray relational analysis and artificial bee colony-support vector machine algorithm. The methods are applied to the case study of load forecasting and verified the effectiveness. The paper is well-written, however, the following points need to be clarified:
1. Page 10, K-means clustering method is used to find the sets of similar days. But this method applied to the short-term load forecast is not clearly explained.
2. Similarly, the gray correlation analysis is used to get the correlation between the predicted days and historical ones. So why are the gray correlation analysis and K-means methods both applied, since they have similar functions?
3. Page 17, what is PSO short for? Particle swan algorithm? Figure 11 and Table 8 compare the performance of the two methods. But the PSO is a relatively traditional method. Other advanced methods for forecasting should be compared with the proposed method and the computational efficiency should be compared.
the paper contain the proper references to existing literature, and is sufficiently concise and organized.
the experimental methods are described clearly and with sufficient details.
the author`s conclusions are justified by the data. The
statistical treatment of the data is adequate, and the data is understandable.
The article meets the PeerJ criteria and should be accepted as is.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.