Improved Structural Local Thermal Energy Planning Based on Prosumer Profile: Part A
Round 1
Reviewer 1 Report
There are minor typos in the abstract concerning the 9, 10 and 11 that appear between the text. Additionally, minor misspelling and grammar errors should be corrected throughout the manuscript.
Overall similarity in Turnitin yields a value of 7 %. However, many paragraphs are written somewhat redundant and should be corrected by a native English speaker, for example: “High energy consumption per m2 of habitable area (energy-intensive user), medium energy consumption per m2 of habitable area (user of medium energy intensity), and low energy consumption per m2 of habitable area (low energy intensity user)” at section 2.1.1.
Latex code for Equation (1) is not properly applied, many errors like this can be found on the manuscript. Authors should use the MDPI template properly to avoid these mistakes.
I do not fully understand Table 1. How is it relevant to the Current methodologies’ subsection?
Section 2.2.1, what does in mean by “Euclidean distances is computationally intensive”, what characteristics should a computer meet to perform these operations?
Equations (2) , (3), (4) and (9) are unreadable. Equations (5), (6) and (7) are not clear.
Figure 2 should be enhanced; font size and style do not match that of the manuscript. Data tips are no recommended in this kind of figures. This also applies to Figures 3 and 4. Furthermore, captions need to be more explicit regarding the results that are being illustrated.
Authors need to significantly improve Section 3. The contribution of this work is not clear. Conclusions lack of support, I can't fully understand what was the mathematical model that was being introduced. Novelty cannot be addressed as their results are not properly compared.
Author Response
There are minor typos in the abstract concerning the 9, 10 and 11 that appear between the text. Additionally, minor misspelling and grammar errors should be corrected throughout the manuscript.
The authors would like to thank the reviewer for the time spent to study their paper and the effort he/she put in to address such interesting topics. An extensive text editing followed the reviewer’s suggestion, where grammar and typos were found and corrected. Unfortunately, the software used by the authors for the text formatting in that case presents inefficiency to normally present the equations. However, the pdf version of the paper, which is also uploaded, includes all the equations in the desired way.
Overall similarity in Turnitin yields a value of 7 %. However, many paragraphs are written somewhat redundant and should be corrected by a native English speaker, for example: “High energy consumption per m2 of habitable area (energy-intensive user), medium energy consumption per m2 of habitable area (user of medium energy intensity), and low energy consumption per m2 of habitable area (low energy intensity user)” at section 2.1.1.
The authors attempted to mitigate redundant paragraphs and to stick to the point (e.g. lines 99-100). Additionally, a text-correcting application was used (Grammarly Editor) which helped correct any syntactical and grammar mistakes (the output full-text report for the paper was 98%, which is the best the authors could do to for the case)
Latex code for Equation (1) is not properly applied, many errors like this can be found on the manuscript. Authors should use the MDPI template properly to avoid these mistakes.
As answered in comment 1.
I do not fully understand Table 1. How is it relevant to the Current methodologies’ subsection?
Table 1 presents the typical questionnaire of RenewIslands methodology, which is one of the currently used methods for designing energy communities. The authors felt that they should have included it because they address and answer it later in Section 3.
Section 2.2.1, what does in mean by “Euclidean distances is computationally intensive”, what characteristics should a computer meet to perform these operations?
The repetitive process of calculating the mean distance of two or more points in a Cartesian system of coordinates, tends to be computationally intensive, especially when the number of points is extensive. The characteristics a computer must have in order to solve such extensive issues are: core ~i5 or more, RAM>8~16 GB.
Equations (2) , (3), (4) and (9) are unreadable. Equations (5), (6) and (7) are not clear.
As answered in comment 1.
Figure 2 should be enhanced; font size and style do not match that of the manuscript. Data tips are no recommended in this kind of figures. This also applies to Figures 3 and 4. Furthermore, captions need to be more explicit regarding the results that are being illustrated.
Figure 2, 3 and 4 are enhanced. The font sizes have increased and the style now matches the one of the manuscript.
Authors need to significantly improve Section 3. The contribution of this work is not clear. Conclusions lack of support, I can't fully understand what was the mathematical model that was being introduced. Novelty cannot be addressed as their results are not properly compared.
The authors feel they should address each point of that comment explicitly. Firstly, Section 3 has been re-approached and some changes were made in order to better rectify the contribution of the work. Secondly, the Conclusions section has been improved significantly (see lines 320-324, 333-355). Lastly, the mathematical model presented in the current study, attempts two things: i) to use 2 existing and proven tools for local energy communities design and ii) to pinpoint their weak spots – combine them into a new holistic approach which uses the best qualities of both of them. The mathematical model attempts to unify qualitative and quantitative metrics and for that to be done, the authors use extensive mathematical tools, such as clustering methods, fuzzy logic etc. The authors hope that the explanation given responded to the reviewer’s concerns and to want to thank him/her for the extensive review of their work.
Reviewer 2 Report
My review of the paper is from the perspective of my own academic background in architecture, which has not previously been extensively trained in the mathematical heuristic methods discussed. I think I understood that the particular focus of the paper is to bring out the value of combining two hitherto more or less isolated methods of data clustering of prosumer energy profiles, Kaya Idaentity and Renew Islands, leading to a more accurate energy smart -Aim grid simulation. The proposed merging of these mathematical methods seems quite promising, but from my perspective as a "pure architect", parallel simulations of energy-minimizing changes to building and utility structures may be even more beneficial.
Author Response
My review of the paper is from the perspective of my own academic background in architecture, which has not previously been extensively trained in the mathematical heuristic methods discussed. I think I understood that the particular focus of the paper is to bring out the value of combining two hitherto more or less isolated methods of data clustering of prosumer energy profiles, Kaya Idaentity and Renew Islands, leading to a more accurate energy smart -Aim grid simulation. The proposed merging of these mathematical methods seems quite promising, but from my perspective as a "pure architect", parallel simulations of energy-minimizing changes to building and utility structures may be even more beneficial.
The authors would like to thank the reviewer for the insights. They are already in the process of accessing and comparing scenarios of LEC creations versus building reformations. Costs, performance etc. It really is a very interesting suggestion.
Reviewer 3 Report
The article addresses a relevant topic - Local Energy Communities as an important part for supporting the energy transition.
However, in the introduction, it could be shown more clearly why the introduced methodology for classifying energy prosumers is so important. Otherwise, one could have the impression that the approach for classifying prosumers into "only" three different groups could be achieved with less effort than introduced/suggested.
Concerning the research design, it is not completely clear whether the chosen 8 dorms (four pairs of identical buildings) support the study or even over-simplify the whole setting. Maybe a larger set of buildings with more different characteristics would depict more clearly the advantages of the introduced approach.
Presentation of results should be improved: fonts (axis descriptions) in Fig. 2 and Fig. 3 should be increased for making them readable.
In Fig. 4, different line types could be used for increasing the readability with black&white printing (colored lines are difficult to differentiate in that case).
Text in second paragraph on page 2 needs reformatting; the same is true for the second paragraph on page 12.
Some reformatting of Eq. (1) and (4) would also ease readability.
Eq. (2) would benefit from explanation in text form.
In last paragraph of Sec. 2.2.4, an "f" is missing with "of".
Descriptions of Eq. (4) and (9) show some overlapping characters (top and bottom paragraphs of page 5).
Last paragraph (second sentence) in the conclusions sections contains two instances of "qualitative" -> one should be "quantitative".
Ref. nr. 33 is missing author information.
Author Response
The article addresses a relevant topic - Local Energy Communities as an important part for supporting the energy transition.
However, in the introduction, it could be shown more clearly why the introduced methodology for classifying energy prosumers is so important. Otherwise, one could have the impression that the approach for classifying prosumers into "only" three different groups could be achieved with less effort than introduced/suggested.
The authors would like to thank the reviewer for the time spent to study their paper and the effort he/she put in to address such interesting topics. In order to address the 1st comment, the authors added in lines 66-69 and 349-355, a more concise explanation of why their work is novel and important compared to the existing solutions.
Concerning the research design, it is not completely clear whether the chosen 8 dorms (four pairs of identical buildings) support the study or even over-simplify the whole setting. Maybe a larger set of buildings with more different characteristics would depict more clearly the advantages of the introduced approach.
The case study presented in this paper is a fully-functioning LEC which operates in Kimmeria, Xanthi. It instills the characteristics of a first-attempt solution as it navigated the authors towards the mathematical model structuring. However, the tool is already been tested in more case studies (one from the Netherlands and one from the Belgium), in data the authors have access because of their involvement with Horizon 2020 research programs. The preliminary results of that study will be published hopefully later this year.
Presentation of results should be improved: fonts (axis descriptions) in Fig. 2 and Fig. 3 should be increased for making them readable.
The authors have corrected it accordingly.
In Fig. 4, different line types could be used for increasing the readability with black&white printing (colored lines are difficult to differentiate in that case).
The authors feel that the presentation of Fig. 4 encapsulates the importance of their message.
Text in second paragraph on page 2 needs reformatting; the same is true for the second paragraph on page 12.
The reformations have been made (green text).
Some reformatting of Eq. (1) and (4) would also ease readability.
Unfortunately, the software used by the authors for the text formatting, in that case, presents inefficiency to present the equations normally. However, the pdf version of the paper, which is also uploaded, includes all the equations in the desired way.
Eq. (2) would benefit from an explanation in text form.
The authors have added the explanation (line 154).
In last paragraph of Sec. 2.2.4, an "f" is missing with "of".
The authors corrected accordingly (line 166).
Descriptions of Eq. (4) and (9) show some overlapping characters (top and bottom paragraphs of page 5).
Unfortunately, the software used by the authors for the text formatting, in that case, presents inefficiency to present the equations normally. However, the pdf version of the paper, which is also uploaded, includes all the equations in the desired way.
Last paragraph (second sentence) in the conclusions sections contains two instances of "qualitative" -> one should be "quantitative".
The authors corrected accordingly (line 356).
Ref. nr. 33 is missing author information.
The authors added the missing info (because of the addition of one reference above ref. 33, the current number of the changed citation is ref. 34).
Reviewer 4 Report
This article has a clear aim, research question, justification, and thesis. It contains significant information regarding the improved structural local thermal/cooling energy planning based on prosumer profile. The argument of the article is based on primary sources and mainly the author/s reference secondary sources. In this sense, the paper is as original as we would expect specifically for publication in the Applied Sciences Journal. In my opinion, it can be improved in the following two dimensions: a. The authors should make a significant text editing (the overall appearance of the paper), b. Minor improvements in the quality of references and content. For example, the author/s can make a reference to the European Green Deal. See, for example, Maris, G.; Flouros, F. The Green Deal, National Energy and Climate Plans in Europe: Member States’ Compliance and Strategies. Adm. Sci. 2021, 11, 75. https://doi.org/10.3390/admsci11030075
.
Author Response
This article has a clear aim, research question, justification, and thesis. It contains significant information regarding the improved structural local thermal/cooling energy planning based on prosumer profile. The argument of the article is based on primary sources and mainly the author/s reference secondary sources. In this sense, the paper is as original as we would expect specifically for publication in the Applied Sciences Journal. In my opinion, it can be improved in the following two dimensions: a. The authors should make a significant text editing (the overall appearance of the paper),
The authors would like to thank the reviewer for the time spent studying their paper. Extensive text editing was done to the whole text and many typos, as well as grammar mistakes, were found and corrected.
Minor improvements in the quality of references and content. For example, the author/s can make a reference to the European Green Deal. See, for example, Maris, G.; Flouros, F. The Green Deal, National Energy and Climate Plans in Europe: Member States’ Compliance and Strategies. Adm. Sci.2021, 11, 75. https://doi.org/10.3390/admsci11030075
The authors are thankful for the suggestion as they feel their work lacked some general strategy citation to guide their points. The reference was added and is in re. nr. 6. Additionally, due to the comment of the reviewer, the authors spotted a missing author information in re. nr 34.
Reviewer 5 Report
This manuscript addresses an interesting issue regarding the energy classification of local communities. This is a hot topic in the international arena, due to the energy intensity of the building sector and the urgency of energy and emissions mitigation for climate purposes. The problem and goals to be addressed are adequately framed in the manuscript, and the research is presented in a transparent and comprehensive way. A novel methodology integrating available qualitative and quantitative methods further coupled with different mathematical tools is proposed. Moreover, an application of the methodology to a real case study in a local energy community in Greece is presented. Finally, the manuscript is well-structured and includes a comprehensive list of references. These are the main strengths of the manuscript. Nonetheless, I raise a few questions below and include a few comments which I think may improve the overall quality of the submitted document:
- I think the term “heating” could be used with advantage instead of the word “thermal” (e.g. in the title of the manuscript). The expression “thermal energy” is broader and is directly related to heating and cooling;
- Page 4: “After the application o k-means…” should read “After the application of k-means…”;
- Page 6, lines 13-18: Isn´t there a conflict, or at least a difficult choice, if e.g. two triangles have equal amounts of data and the one with the lowest category is the one that agrees with the RenewIsland class?
- Isn´t the year (yr) missing in the units for specific thermal energy consumption?
- Table 2: I couldn´t get how the authors calculated Eresid values. For building A1 e.g. I reach 221,99 MWh/yr;
- Page 8: “Some assumptions were be made to…” should read “Some assumptions were made to…”;
Figure 2: please explain in more detail this figure. For instance, selection of a single cluster corresponds to a dataset with 14 points?
Author Response
This manuscript addresses an interesting issue regarding the energy classification of local communities. This is a hot topic in the international arena, due to the energy intensity of the building sector and the urgency of energy and emissions mitigation for climate purposes. The problem and goals to be addressed are adequately framed in the manuscript, and the research is presented in a transparent and comprehensive way. A novel methodology integrating available qualitative and quantitative methods further coupled with different mathematical tools is proposed. Moreover, an application of the methodology to a real case study in a local energy community in Greece is presented. Finally, the manuscript is well-structured and includes a comprehensive list of references. These are the main strengths of the manuscript. Nonetheless, I raise a few questions below and include a few comments which I think may improve the overall quality of the submitted document:
- I think the term “heating” could be used with advantage instead of the word “thermal” (e.g. in the title of the manuscript). The expression “thermal energy” is broader and is directly related to heating and cooling;
The authors would like to thank the reviewer for the time spent to study their paper and the effort he/she put in to address such interesting topics. The aim of the authors is to address both heating and cooling needs. Therefore, they chose the option of referring to the subject as “thermal energy” as they feel it better encapsulates the meaning and intent of their work. Such changes have been made throughout the text and of course the title.
- Page 4: “After the application o k-means…” should read “After the application of k-means…”;
The authors corrected accordingly (line 166).
- Page 6, lines 13-18: Isn´t there a conflict, or at least a difficult choice, if e.g. two triangles have equal amounts of data and the one with the lowest category is the one that agrees with the RenewIsland class?
In that case, the HDLM prevails again as a more concise and quantitative approach. If Low and Medium triangles contain equal amounts of data e.g., and the RenewIslands suggest the Low energy class, then the final choice is the Medium energy class.
- Isn´t the year (yr) missing in the units for specific thermal energy consumption?
The specific thermal energy consumption is the main indicator of the consumption of energy of each building type and use of spaces, according to the square footage area of the building. That indicator applies to heated areas and has as an S.I. unit of measurement kWhth/m2.
- Table 2: I couldn´t get how the authors calculated Eresid values. For building A1 e.g. I reach 221,99 MWh/yr;
The formula of Kaya Identity methodology can be used for the case as follows, [(No. of residents*m2 of heated area)*(m2 of heated area / No. of residents)*Spec. ener. Cons./1000].
- Page 8: “Some assumptions were be made to…” should read “Some assumptions were made to…”;
The authors corrected accordingly (line 239).
Figure 2: please explain in more detail this figure. For instance, selection of a single cluster corresponds to a dataset with 14 points?
The elbow diagram in Figure 2 has one purpose. To find the ideal number of clusters and feed the clustering algorithm with a cluster number in which the data will be grouped. The more clusters we choose, the fewer data will be included in each cluster. The elbow diagram serves as a tool to find the optimal number of clusters for the dataset under study. The number of data in all the clusters together is the total number of data for each examined case. An attempt to further elaborate and explain is given in lines 246-249.
