Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree

Round 1

Reviewer 1 Report

The article presents a load forecasting model based on regression trees. While the article is of interest to the readers it is poorly structured and written. 

 

1. The concept of Regression trees has to be explained more elaborately. 

 

2. What is the reason for choosing regression trees ? there are numerous forecasting algorithms to choose from. 

 

3. Generally, research papers present more than one method and prove the superiority of their approach. There is one line saying it is better performing that linear regression but I see no results from this method. 

 

4. More analysis is required. How does this model perform between weekends and weekdays ? How does does it perform under different seasons ? 

 

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.docx

Reviewer 2 Report

The work concerns the prediction of the electrical energy of a load according to a concept, for one hour or more. the work is topical and authors must:

- Improve the abstract and the conclusion by mentioning their contribution in this field,

- Discuss the results obtained in relation to the literature, in terms of precision of prediction

- Discuss their contributions in this area.

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.docx

Reviewer 3 Report

This paper proposes a short-term electric power load forecasting method and the final simulation results have verified the effectiveness of the proposed method. This paper is well organized and presented. However, I have some comments as following:

1.     Please make a strong effect to improve the current Abstract and give more descriptions about the motivation, significance and main contributions of this research in Abstract.

2.     In Section I, please try to improve the first paragraph as the current description for short-term load forecasting is limited. In addition, the existing problem and major challenges need to be further discussed in the second paragraph.

3.     The current literature review must be improved with more comprehensive studies to support the motivation of this paper. At the same time, the contribution part that cannot fully reflect the main contributions of this paper should be improved.  

4.     In Section I, please discuss this prediction method over others: predictive voltage hierarchical controller design for islanded microgrids under limited communication, delay-tolerant predictive power compensation control for photovoltaic voltage regulation.

5.     I wonder if the authors use historical data preprocessing or valuable data extraction in the forecasting process. If so, please give the detailed method steps.

6.     The current presentation of some figures such as Figure 1，Figure 2, Figure 5 and Figure 6 should be improved with larger font. At the same time, it is difficult to find that in which system (energy management system?) this prediction method is applied, so please add a figure with actual scenario and system configuration.

7.     The authors are suggested to give more comparison results with other representative methods in Section 2, highlighting the main advantage and better performance of the proposed method.

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have addressed the points that were highlighted. 

Reviewer 2 Report

The pap

 

The paper is improved following my suggestions.