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Sustainability
Volume 15
Issue 14
10.3390/su151410798
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Article
Peer-Review Record

Deep Learning Method for Evaluating Photovoltaic Potential of Rural Land Use Types

Sustainability 2023, 15(14), 10798; https://doi.org/10.3390/su151410798
by Zhixin Li 1, Chen Zhang 2, Zejun Yu 3, Hong Zhang 1,* and Haihua Jiang 4
Reviewer 1:
Gabriel Takyi
Reviewer 2:
Daijun Feng
Sustainability 2023, 15(14), 10798; https://doi.org/10.3390/su151410798
Submission received: 1 May 2023 / Revised: 5 July 2023 / Accepted: 6 July 2023 / Published: 10 July 2023
(This article belongs to the Section Sustainable Urban and Rural Development)

Round 1

Reviewer 1 Report

The work is very interesting and seeks to evaluate the PV potential of different land use types using deep learning. 

My general impressions about the various sections are summarised below:

Abstract:

The abstract is good but contains a few errors. For example:

Lines 13-14, facilitate the overall ........this is incomplete statement.

The first statement of the abstract must reflect the title. That does not seem to the the case here.

Introduction: 

The background and justification of the work have been clearly established with appropriate references.

Lines 47-49 needs checking...

Data and method:

Line 136:- remove therefore, it's repeated

Figure 6 needs further explanation.

Results:

Figure 10 needs further explanation.

Line 335-337.....how do you see the information from the figure?

Discussion & conclusion: are adequate

The entire paper needs improvement in terms of the technical language of the field (English language).

Do not use And to begin a sentence.

The technical language of the field (English Language) needs improvement in several places.

Author Response

Please see the attachment. 

Author Response File: Author Response.docx

Reviewer 2 Report

In this manuscript, the authors established deep-learning method for rapidly assessing solar energy potential of different types of land in the rural area in Wuhan, Hubei province in China. This method presents excellent applicability to flexible scale PV generation analysis of readily available satellite image with high precision in a high-speed calculation.  And this method helps to clarify the distribution of solar potential of different types of installable surface in rural areas: roofs, water surfaces, and wastelands and suitable areas, respectively. This study improves the scientific and implementable nature of solar energy planning. Therefore, I recommend this work to be published in Sustainability after writing error correction.

 

 

 

Comments for author File: Comments.pdf

Some English language errors needed to be corrected. 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

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