A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models

Round 1

Reviewer 1 Report

Comments to the authors:

First, I am grateful for the opportunity offered me to review this manuscript by the editor of Applied Sciences journal for recognizing me and allowing me to make recommendations on research paper. I’m very pleased to do my comments regarding the provided article. This is a very interesting study entitled “A Novel Hybrid Method for Short Term Wind Speed Prediction based on Wind Probability Distribution Function and Machine Learning Models.” The following major comments are listed for the authors to consider:

1. For the wide readership of the journal, the manuscript should be proofread to improve the linguistic quality, including spellings, grammars, and some long sentences which are not easy to follow.

2. Abstract is an important part of the paper, which can reflect the core points of the paper. It is suggested that the author modify the abstract to increase the readability of the paper.

3. In abstract, “In this manuscript, the development of an error correction algorithm for the probability density-based wind speed prediction model is introduced, also a comparative analysis of the performance of each method for accurately predicting wind speed, and for each time step of short-term forecast horizons is performed.” break into two sentences.

4. In abstract, the abbreviation of “National Oceanic and Atmospheric Administration” should be added.

5. The keywords should be arranged in alphabetically ascending order.

6. The statement of novelty is insufficient and must be added at the end of the "Introduction" section.

7. Review the following papers:

i) Shahani, N. M., Kamran, M., Zheng, X., & Liu, C. (2022). Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA. Petroleum Science and Technology, 40(5), 534-555.

ii) Shahani, N. M., Zheng, X., Guo, X., & Wei, X. (2022). Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield. Sustainability, 14(6), 3689.

 

8. Python codes for “Figure 2. Distribution of wind speed and direction at four different sites” must be shared to check the more clarity of data.

9. Performance Evaluation of Models, namely MAE, RMSE and MAPE must be cited.

10. The authors need to use a-20 index or a-10 index in their analysis. Please refer to the papers in comment 7.

11. To well understand the influence of each input parameter on the output. I highly suggest applying sensitivity analysis, i.e., Multiple Parametric Sensitivity Analysis (MPSA).

 

12. The text size and style of the figure should be consistent throughout the manuscript.

Author Response

Thank you so much for your insightful comments. Please see the attachment for point by point response. 

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors introduced the development of an error correction algorithm for the probability density-based wind speed prediction model, as well as a comparison examination of the performance of each technique for properly predicting wind speed at each time step of short-term forecast horizons. The paper is well-written and well-structured. Here are my comments.

1- Please rewrite the abstract section in terms of abbreviation and hierarchy (why, gap, how, what). Also, in the abstract and conclusion section, use absolute terms to support your findings rather than relative terms.

2-There are not enough references in the Introduction section. The authors are encouraged to include these significant references in the introduction section:

https://www.sciencedirect.com/science/article/pii/S0010482521009355

https://link.springer.com/article/10.1007/s00521-022-07424-w

https://www.sciencedirect.com/science/article/abs/pii/S2210670722004061

3-The structure of the paper in the introduction section is lacking. Add it to the introduction section's final paragraph.

4-Please appropriately cite any formulas borrowed from other papers.

5-Add a notion table to help readers grasp the formulas.

6-In the conclusion section, please elaborate on the implications of the work, the disadvantages of the proposed method, and future works.

Author Response

Thank you so much for your comments. It helps us to make the manuscript more meaningful. Please see the attachment. 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The paper can be accepted now.

Reviewer 2 Report

It can be accepted.