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Applied Sciences
Volume 9
Issue 13
10.3390/app9132630
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Article
Peer-Review Record

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

Appl. Sci. 2019, 9(13), 2630; https://doi.org/10.3390/app9132630
by Le Thi Le 1,*, Hoang Nguyen 2,*, Jie Dou 3 and Jian Zhou 4
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Appl. Sci. 2019, 9(13), 2630; https://doi.org/10.3390/app9132630
Submission received: 3 June 2019 / Revised: 26 June 2019 / Accepted: 27 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)

Round 1

Reviewer 1 Report

Dear Authors


Thank you for your work that presents an interesting comparison and useful results. 

I would recommend to present more details about the application and future work that can be carried out based on your results.

By doing so, not only the reader gains insight in the possible applications and use of the results, but also he may be encouraged to apply your results. 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

I have carefully reviewed this paper. It is an interesting problem, and it was very well written. A comprehensive comparison and assessment of multiple meta-heuristics algorithms with an ANN model were implemented for estimating the heating load of energy efficiency of buildings for smart city planning, including PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN. It was within the scope of the special issue. Some revisions need to be added/addressed before publishing:

1. Suggest expand the introduction part as well as the results of the previous study.
2. How to determine the input and output variables?
3. Background of the PSO algorithm should be more details.
4. Why divide the dataset into two parts according to the 80/20 ratio? How to divide?
5. Which technique was used to avoid overfitting for the initial ANN model?
6. Suggest analyzing the sensitivity of the input variables.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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