Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model

Round 1

Reviewer 1 Report

The work is interesting. However, the use of data needs to be clarified. The publication shows that the input data for network learning is 40 sets of 22 elements. Hence, the sample size of the collection for ANN to enter the generalization mode seems to be much too small. Therefore, complementary methods of analysis of set elements should be used to minimize the number of elements in the set., or increase the number of harvests. Or use another method of analysis, e.g. AHC.

Author Response

We have revised the manuscript according to your valuable comments. Please see the attachment for the specific reply. Thanks again for your advice!

Author Response File: Author Response.docx

Reviewer 2 Report

The paper presents results of a study smart grid project benefit evaluation which is based on improved hybrid model. Authors indicate the role of the construction and development of smart grids, which have significant influence on safe and stable operation of power systems. The paper presents new hybrid intelligent evaluation model based on improved TOPSIS and long-short term memory. Authors established a set of smart grid benefit evaluation index system. Next, they established evaluation model, which was based on entropy weight method and improved TOPSIS.

 

Dear authors. Thank you very much for interesting paper about smart grid project benefit evaluation which is based on improved hybrid model. I have some comments and questions, which should be included and answered in the paper.

 

Questions and suggestions:

 

1. Introduction explains significant role of smart grid, what is direct effect of intelligent city development last years. Evaluation method for smart grid is described using correct references. Also problems, which occur with smart grid, are presented.

2. I did not find simple definitions and fundamental information according smart grid. Please extend Introduction chapter if possible.

3. Chapter 2 is focused on construction of benefit evaluation index system of mentioned grid. Authors present safe reliability problem, and efficient intelligence. They mentioned about new sources of energy, what is good. Anyway, they do not include problems, which occur the use of renewable energy, what probably means new sources. Please consider to put the information in chapter 2.

4. in case of environmental and economic benefits, I would expect some tables and figures, where numbered benefits should be clear presented. Please explain it if possible.

5. The chapter Evaluation Method is well organized, with property formulas. I did not find simple definition of mentioned method. Please start the chapter using the definition of the method, you describe.

6. The chapter Example Analysis and Discussion is interesting. But presented results are not clear. For example – table 3, and others, shows some values without any units. Please complete if possible.

7. Figure 5 – presented characteristics are very similar. I cannot see any trends and differences between them. How do authors want to explain it?

Author Response

We have revised the manuscript according to your valuable comments. Please see the attachment for the specific reply. Thanks again for your advice!

Author Response File: Author Response.docx

Reviewer 3 Report

The topic of the presented paper is interesting.

 

The paper under review is devoted new hybrid intelligent evaluation model based on improved TOPSIS and long-16 short-term memory (LSTM) optimized by a modified sparrow search algorithm (MSSA) for Smart Grid Project Benefit Evaluation. The data are presented clearly, quality of tables and figures is sufficient enough. Although the paper looks scientifically, it has some issues I would like to indicate.

1.     To begin with, Used English needs proofreading to avoid typos and correct sentence structures

 

2.  The authors claim in lines 377 -380 that the sparrow search algorithm has fast convergence speed, good stability, and robustness compared with other swarm intelligence optimization algorithms. Briefly give a justification, of how SSA is better as compared to other optimization techniques.

3.       Pseudocode of improved SSA is missing.

4.  Some important parameters like. “Power supply restoration time” is the main parameter of power system reliability which is expressed in indices like SAIFI, SAIDI, etc. So, the Authors must expand the introduction by describing these parameters as well.

 

5.       It is not understandable for the reviewer from where the data has been collected i. e any references 

 

Author Response

We have revised the manuscript according to your valuable comments. Please see the attachment for the specific reply. Thanks again for your advice!

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The corrected work is logically consistent and the results are presented appropriately. The work is a pleasure to read. Congratulations on the results of your fruitful work.

Author Response

Thank you for your affirmation. Thanks again for your advice.