Harnessing the Power of Artificial Intelligence for Collaborative Energy Optimization Platforms

Round 1

Reviewer 1 Report

Dear Authors

The theme of the manuscript energies-2469218 firstly highlights the importance of energy in sustainable development and investigates how AI tools can catalyze the next phase of human civilization. We can observe that the research conducted sought to demonstrate the effectiveness of applying MCDA methods, specifically AHP and TOPSIS, in evaluating and ranking the performance of five collaborative energy optimization platforms. The theme is relevant, the MCDA tools are adequate, however I observe opportunity for improvement, before being considered for publication. Listed below are the proposed suggestions:

1. In the abstract the authors state at the end that "The research provides valuable insights for decision makers and researchers in the field, promoting the development and implementation of more efficient and sustainable AI-based energy systems." I suggest you succinctly detail what insights are provided to decision makers.

2. I suggest merging sections 2 and 3 into one, called the literature review, and detailing how you selected the texts that make it up.

3. In relation to section 4, the methodology needs to be better detailed, I suggest describing the method step-by-step. As well as, the insertion of a diagram that illustrates the methodology.

4. I suggest the authors to justify the choice of the methods used in detriment of other methods, both of weighting and rank weighting. Consider the following paper for this answer: https://doi.org/10.3390/info14050285

5.  The section 5 that deals with the results needs to be better detailed and discussed.

Good review.

Reviewer

Author Response

Manuscript Energies-2469218

Responses for Reviewers

Dear Reviewer,

We would like to express our appreciation for the reviews. Thank you very much for suggestions, which were clear and very accurate. We made the necessary corrections. We have incorporated all the suggestions because we agreed with them, and thank you especially for such good suggestions to improve our article.

We would like to refer to the detailed reviewer’s suggestions below:

The theme of the manuscript energies-2469218 firstly highlights the importance of energy in sustainable development and investigates how AI tools can catalyze the next phase of human civilization. We can observe that the research conducted sought to demonstrate the effectiveness of applying MCDA methods, specifically AHP and TOPSIS, in evaluating and ranking the performance of five collaborative energy optimization platforms. The theme is relevant, the MCDA tools are adequate

Authors’ response: Thank you for the positive reception of the article.

 

However I observe opportunity for improvement, before being considered for publication. Listed below are the proposed suggestions:

1. In the abstract the authors state at the end that "The research provides valuable insights for decision makers and researchers in the field, promoting the development and implementation of more efficient and sustainable AI-based energy systems." I suggest you succinctly detail what insights are provided to decision makers.

Authors’ response: We made the necessary corrections. The research provides valuable insights for decision makers and researchers in the field, promoting the development and implementation of more efficient and sustainable AI-based energy systems by:

• The importance of collaboration: Decision makers will gain an understanding of the significance of collaboration among energy distribution companies, policymakers, and consumers. This insight emphasizes the need for cooperative efforts to optimize energy management and achieve energy efficiency goals.
• The value of data-sharing: Decision makers will recognize the importance of efficient data-sharing among stakeholders. This insight highlights the role of data exchange in facilitating informed decision-making and identifying opportunities for energy optimization.
• The potential of AI algorithms: Decision makers will gain insights into the integration of AI algorithms in energy systems. This insight emphasizes the power of AI for advanced data analytics, predictive modeling, and optimization techniques, enabling more effective decision-making and resource allocation.
• Implications for energy efficiency: Decision makers will understand how the CEOP model can enhance energy efficiency. This insight emphasizes the identification of energy wastage, implementation of demand response strategies, and overall improvement of energy distribution system efficiency.
• Cost reduction strategies: Decision makers will learn about data-driven decision-making and its role in reducing costs. This insight highlights how optimal resource allocation and operational planning, facilitated by the CEOP model, can lead to cost savings.
• Grid stability enhancement: Decision makers will gain insights into how the CEOP model contributes to improved grid stability. This insight emphasizes the model's ability to address supply-demand imbalances and support the integration of renewable energy sources, ultimately leading to a more stable and reliable grid.

 

2. I suggest merging sections 2 and 3 into one, called the literature review, and detailing how you selected the texts that make it up.

Authors’ response: As recommended, we combined both sections into a single section called the literature review and expanded the scholarly bibliography review itself.

 

3. In relation to section 4, the methodology needs to be better detailed, I suggest describing the method step-by-step. As well as, the insertion of a diagram that illustrates the methodology.

Authors’ response: Within this section, we have included the description of the methodology that was in the appendix so that the methodology is thoroughly covered in one section. The methodology has been clarified and more precisely described.

 

4. I suggest the authors to justify the choice of the methods used in detriment of other methods, both of weighting and rank weighting. Consider the following paper for this answer: https://doi.org/10.3390/info14050285

Authors’ response: We made the necessary corrections. The theoretical background has been added (broader background literature) and the main points have been summarized in 'key points'. The analysis was extended based on new sources, including those indicated by other reviewers. We have also reviewed the suggested bibliographic items and added them in the appropriate places, as well as other current references on the topic in question among scientific journals.

1. Gidron, B.; Cohen-Israel, Y.; Bar, K.; Silberstein, D.; Lustig, M.; Kandel, D. Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions. Sustainability, 2021, 13, 10048.
2. Ayan, B.; Abacıoğlu, S.; Basilio, M.P. A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making. Information 2023, 14, 285. https://doi.org/10.3390/info14050285.
3. Copikova, ‘The Methodology Proposal of a Competence Models Creation Using the Ahp Method and Saaty’s Method of Determining Weights’, in Hradecke Ekonomicke Dny 2014: Ekonomicky Rozvoj a Management Regionu, Dil I, P. Jedlicka, Ed., Hradec Kralove: Gaudeamus, 2014, pp. 165–172. Accessed: Feb. 01, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/full-record/WOS:000398250000022
4. L. Tung and S. L. Tang, ‘A comparison of the Saaty’s AHP and modified AHP for right and left eigenvector inconsistency’, Eur. J. Oper. Res., vol. 106, no. 1, pp. 123–128, Apr. 1998, doi: 10.1016/S0377-2217(98)00353-1.
5. Coffey and D. Claudio, ‘In defense of group fuzzy AHP: A comparison of group fuzzy AHP and group AHP with confidence intervals’, Expert Syst. Appl., vol. 178, p. 114970, Sep. 2021, doi: 10.1016/j.eswa.2021.114970.
6. -J. Wang, ‘Interval-Valued Fuzzy Multi-Criteria Decision-Making by Combining Analytic Hierarchy Process with Utility Representation Function’, Int. J. Inf. Technol. Decis. Mak., vol. 21, no. 05, pp. 1433–1465, Sep. 2022, doi: 10.1142/S0219622022500225.
7. Miciuła, J. Nowakowska-Grunt, Using the AHP Method to Select an Energy Supplier for Household in Poland. Procedia Comput. Sci.2019, 159, 2324–2334.
8. Turk and M. Ozkok, ‘Shipyard location selection based on fuzzy AHP and TOPSIS’, J. Intell. Fuzzy Syst., vol. 39, no. 3, pp. 4557–4576, 2020, doi: 10.3233/JIFS-200522.
9. L. Saaty, Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. RWS Publications, 2012.
10. M. Vadivel, A. H. Sequeira, S. K. Jauhar, and V. Chandana, ‘Tamilnadu Omnibus Travels Evaluation Using TOPSIS and Fuzzy TOPSIS Methods’, in Innovations in Bio-Inspired Computing and Applications, Ibica 2021, A. Abraham, A. M. Madureira, A. Kaklauskas, N. Gandhi, A. Bajaj, A. K. Muda, D. Kriksciuniene, and J. C. Ferreira, Eds., Cham: Springer International Publishing Ag, 2022, pp. 24–31. doi: 10.1007/978-3-030-96299-9_3.
11. Kuttler, B. Cilali, and K. Barker, ‘Destination Selection in Environmental Migration with TOPSIS’, in 2021 Systems and Information Engineering Design Symposium (ieee Sieds 2021), New York: Ieee, 2021, pp. 261–266. Accessed: Mar. 05, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/full-record/WOS:000828133400047

 

5. The section 5 that deals with the results needs to be better detailed and discussed.

Authors’ response: We have made the appropriate corrections with the indications, the comparative analysis with related scientific works was extended. The conclusions section has been improved in terms of content and the whole work in terms of style.

 

We have incorporated all the suggestions made by the reviewers. Those changes are highlighted within the revised manuscript file with tracked changes.

Thanks again for the clear review and suggestions for corrections to improve our article.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors explored the viability and transformative potential of Artificial Intelligence (AI) tools in energy sustainable development in the context of futuristic of energy systems. The authors have identified the appropriate tools to realize their objectives.  Nevertheless, the authors are to give clarification on the below-given comments one by one.

Q1. There is a duplication of information. Check 157-163; and 61-65. You may present it differently in both places.

Q2. The authors have to modify the methodology of the research. A serious look into this will improve the paper.

Q3. The authors have explained how AI could help but thorough research is missing in the paper

Q4. Authors have only explained things superficially but how AI is implemented to achieve the objectives is not mapped

Q5. How AHP and TOPSIS are used in the research is not clear

Q6. The authors have listed the criterion well but the details of the data are missing. You may attach the Appendix if you have it.

Q7. Authors say, "The findings can aid decision-makers, energy practitioners, and researchers" in line 503, but this is discussed in the paper quantitatively.

Q8. The authors say that "Through the analysis, valuable insights into the strengths and limitations of the CEOP models were gained in line 496, so is this paper only test the model CEOP or explore the advantage of AI (its outcome)

Q9. the abstract and conclusions have to be modified with the qualitative analysis to attract the readers.

Author Response

Manuscript Energies-2469218

Responses for Reviewers

Dear Reviewer,

We would like to express our appreciation for the reviews. Thank you very much for suggestions, which were clear and very accurate. We made the necessary corrections. We have incorporated all the suggestions because we agreed with them, and thank you especially for such good suggestions to improve our article.

We would like to refer to the detailed reviewer’s suggestions below:

The authors explored the viability and transformative potential of Artificial Intelligence (AI) tools in energy sustainable development in the context of futuristic of energy systems. The authors have identified the appropriate tools to realize their objectives. 

Authors’ response: Thank you for the positive reception of the article.

Nevertheless, the authors are to give clarification on the below-given comments one by one.

Q1. There is a duplication of information. Check 157-163; and 61-65. You may present it differently in both places.

Authors’ response: We made the necessary corrections.

 

Q2. The authors have to modify the methodology of the research. A serious look into this will improve the paper.

Authors’ response: Within this section, we have included the description of the methodology that was in the appendix so that the methodology is thoroughly covered in one section. The methodology has been clarified and more precisely described.

 

Q3. The authors have explained how AI could help but thorough research is missing in the paper

Authors’ response: We made the necessary corrections in paper. The research provides valuable insights for decision makers and researchers in the field, promoting the development and implementation of more efficient and sustainable AI-based energy systems by:

• The importance of collaboration: Decision makers will gain an understanding of the significance of collaboration among energy distribution companies, policymakers, and consumers. This insight emphasizes the need for cooperative efforts to optimize energy management and achieve energy efficiency goals.
• The value of data-sharing: Decision makers will recognize the importance of efficient data-sharing among stakeholders. This insight highlights the role of data exchange in facilitating informed decision-making and identifying opportunities for energy optimization.
• The potential of AI algorithms: Decision makers will gain insights into the integration of AI algorithms in energy systems. This insight emphasizes the power of AI for advanced data analytics, predictive modeling, and optimization techniques, enabling more effective decision-making and resource allocation.
• Implications for energy efficiency: Decision makers will understand how the CEOP model can enhance energy efficiency. This insight emphasizes the identification of energy wastage, implementation of demand response strategies, and overall improvement of energy distribution system efficiency.
• Cost reduction strategies: Decision makers will learn about data-driven decision-making and its role in reducing costs. This insight highlights how optimal resource allocation and operational planning, facilitated by the CEOP model, can lead to cost savings.
• Grid stability enhancement: Decision makers will gain insights into how the CEOP model contributes to improved grid stability. This insight emphasizes the model's ability to address supply-demand imbalances and support the integration of renewable energy sources, ultimately leading to a more stable and reliable grid.

 

Q4. Authors have only explained things superficially but how AI is implemented to achieve the objectives is not mapped

Authors’ response: We made the necessary corrections. To provide a more detailed mapping of AI implementation in achieving the objectives of the Collaborative Energy Optimization Platform (CEOP), let's explore how AI can be integrated within the model:

1. Data Analytics and Predictive Modeling:

• AI algorithms can be used to analyze large volumes of energy-related data, such as consumption patterns, weather forecasts, and grid performance.
• Machine learning techniques can identify patterns, anomalies, and correlations in the data, enabling insights into energy usage, demand forecasting, and potential efficiency improvements.
• Predictive modeling algorithms can anticipate energy demand fluctuations, optimize supply management, and support grid stability planning.

2. Optimization Techniques:

• AI algorithms, such as optimization algorithms, can be employed to optimize energy management decisions, such as scheduling energy generation, storage, and distribution.
• These algorithms can consider various factors, including real-time energy prices, demand-response capabilities, renewable energy availability, and grid constraints.
• By utilizing optimization techniques, the CEOP model can identify the most cost-effective and efficient energy distribution strategies.

3. Intelligent Energy Management:

• AI-based systems can provide intelligent energy management solutions by continuously monitoring energy usage, detecting inefficiencies, and suggesting optimization measures.
• These systems can employ AI algorithms, such as reinforcement learning or expert systems, to make real-time decisions on load balancing, energy storage utilization, and demand response activation.
• AI can enable automated control systems that adjust energy distribution in response to changing conditions, ensuring optimal utilization of resources and grid stability.

4. Demand-Side Management and Consumer Engagement:

• AI can facilitate demand-side management by analyzing consumer behavior and preferences.
• Smart home devices, equipped with AI algorithms, can learn user patterns, optimize energy usage, and provide personalized recommendations to consumers for energy efficiency improvements.
• AI-powered consumer engagement platforms can provide energy consumption insights, real-time feedback, and incentivize sustainable energy practices, fostering active consumer participation in energy optimization.

5. Decision Support Systems:

• AI can enhance decision-making processes for stakeholders by providing data-driven insights, scenario analysis, and predictive simulations.
• Decision support systems powered by AI algorithms can help policymakers, energy distribution companies, and consumers evaluate the potential impact of various energy management strategies, policies, and investments.
• These systems enable stakeholders to make informed decisions, considering multiple factors such as cost-effectiveness, environmental impact, and grid stability.

By integrating AI in these ways, the Collaborative Energy Optimization Platform (CEOP) can leverage advanced analytics, optimization techniques, and intelligent decision-making to achieve the objectives of enhanced energy efficiency, reduced costs, and improved grid stability.

 

Q5. How AHP and TOPSIS are used in the research is not clear

Q6. The authors have listed the criterion well but the details of the data are missing. You may attach the Appendix if you have it.

Authors’ response: we have included the description of the methodology that was in the appendix so that the methodology is thoroughly covered in one section. The methodology has been clarified and more precisely described. So from the appendix we added the description of the methodology to the article, and we included the details of the data in the appendix.

 

Q7. Authors say, "The findings can aid decision-makers, energy practitioners, and researchers" in line 503, but this is discussed in the paper quantitatively.

Q8. The authors say that "Through the analysis, valuable insights into the strengths and limitations of the CEOP models were gained in line 496, so is this paper only test the model CEOP or explore the advantage of AI (its outcome)

Authors’ response: This paper has a broader focus than solely testing the CEOP model. While it does review and analyze the CEOP model, the authors also aim to explore the advantages and potential outcomes of leveraging AI within the energy sector. The paper likely assesses the strengths and limitations of the CEOP model to understand its effectiveness in achieving the stated objectives of increased energy efficiency, reduced costs, and improved grid stability. Additionally, the analysis likely provides insights into how the integration of AI algorithms within the CEOP model contributes to its overall effectiveness. Therefore, the paper appears to encompass both the evaluation of the CEOP model and the exploration of the advantages of AI within the context of energy optimization.

 

Q9. the abstract and conclusions have to be modified with the qualitative analysis to attract the readers.

Authors’ response: We made the necessary corrections. The theoretical background has been added (broader background literature) and the main points have been summarized in 'key points'. The analysis was extended based on new sources, including those indicated by other reviewers. We have also reviewed the suggested bibliographic items and added them in the appropriate places, as well as other current references on the topic in question among scientific journals.

1. Gidron, B.; Cohen-Israel, Y.; Bar, K.; Silberstein, D.; Lustig, M.; Kandel, D. Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions. Sustainability, 2021, 13, 10048.
2. Ayan, B.; Abacıoğlu, S.; Basilio, M.P. A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making. Information 2023, 14, 285. https://doi.org/10.3390/info14050285.
3. Copikova, ‘The Methodology Proposal of a Competence Models Creation Using the Ahp Method and Saaty’s Method of Determining Weights’, in Hradecke Ekonomicke Dny 2014: Ekonomicky Rozvoj a Management Regionu, Dil I, P. Jedlicka, Ed., Hradec Kralove: Gaudeamus, 2014, pp. 165–172. Accessed: Feb. 01, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/full-record/WOS:000398250000022
4. L. Tung and S. L. Tang, ‘A comparison of the Saaty’s AHP and modified AHP for right and left eigenvector inconsistency’, Eur. J. Oper. Res., vol. 106, no. 1, pp. 123–128, Apr. 1998, doi: 10.1016/S0377-2217(98)00353-1.
5. Coffey and D. Claudio, ‘In defense of group fuzzy AHP: A comparison of group fuzzy AHP and group AHP with confidence intervals’, Expert Syst. Appl., vol. 178, p. 114970, Sep. 2021, doi: 10.1016/j.eswa.2021.114970.
6. -J. Wang, ‘Interval-Valued Fuzzy Multi-Criteria Decision-Making by Combining Analytic Hierarchy Process with Utility Representation Function’, Int. J. Inf. Technol. Decis. Mak., vol. 21, no. 05, pp. 1433–1465, Sep. 2022, doi: 10.1142/S0219622022500225.
7. Miciuła, J. Nowakowska-Grunt, Using the AHP Method to Select an Energy Supplier for Household in Poland. Procedia Comput. Sci.2019, 159, 2324–2334.
8. Turk and M. Ozkok, ‘Shipyard location selection based on fuzzy AHP and TOPSIS’, J. Intell. Fuzzy Syst., vol. 39, no. 3, pp. 4557–4576, 2020, doi: 10.3233/JIFS-200522.
9. L. Saaty, Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. RWS Publications, 2012.
10. M. Vadivel, A. H. Sequeira, S. K. Jauhar, and V. Chandana, ‘Tamilnadu Omnibus Travels Evaluation Using TOPSIS and Fuzzy TOPSIS Methods’, in Innovations in Bio-Inspired Computing and Applications, Ibica 2021, A. Abraham, A. M. Madureira, A. Kaklauskas, N. Gandhi, A. Bajaj, A. K. Muda, D. Kriksciuniene, and J. C. Ferreira, Eds., Cham: Springer International Publishing Ag, 2022, pp. 24–31. doi: 10.1007/978-3-030-96299-9_3.
11. Kuttler, B. Cilali, and K. Barker, ‘Destination Selection in Environmental Migration with TOPSIS’, in 2021 Systems and Information Engineering Design Symposium (ieee Sieds 2021), New York: Ieee, 2021, pp. 261–266. Accessed: Mar. 05, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/full-record/WOS:000828133400047

 

 

We have incorporated all the suggestions made by the reviewers. Those changes are highlighted within the revised manuscript file with tracked changes.

Thanks again for the clear review and suggestions for corrections to improve our article.

Author Response File: Author Response.pdf

Reviewer 3 Report

Figures 1 and 2 are not refered to and discussed in the text.

 

References must be included in Sections 3 and 4. 

 

Appendix A is mentioned, but is nowehere to be found - maybe it is an additional file that the reviewer doesn't have access to? 

 

The contributions of the paper are unclear. Also, is this paper a review of existing models? Or does it imply any implementation? Please clarify. 

 

Author Response

Manuscript Energies-2469218

Responses for Reviewers

Dear Reviewer,

We would like to express our appreciation for the reviews. Thank you very much for suggestions, which were clear and very accurate. We made the necessary corrections. We have incorporated all the suggestions because we agreed with them, and thank you especially for such good suggestions to improve our article.

We would like to refer to the detailed reviewer’s suggestions below:

Figures 1 and 2 are not refered to and discussed in the text.

Authors’ response: We made the necessary corrections.

 

References must be included in Sections 3 and 4. 

Authors’ response: We made the necessary corrections. The theoretical background has been added (broader background literature) and the main points have been summarized in 'key points'. The analysis was extended based on new sources, including those indicated by other reviewers. We have also reviewed the suggested bibliographic items and added them in the appropriate places, as well as other current references on the topic in question among scientific journals.

1. Gidron, B.; Cohen-Israel, Y.; Bar, K.; Silberstein, D.; Lustig, M.; Kandel, D. Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions. Sustainability, 2021, 13, 10048.
2. Ayan, B.; Abacıoğlu, S.; Basilio, M.P. A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making. Information 2023, 14, 285. https://doi.org/10.3390/info14050285.
3. Copikova, ‘The Methodology Proposal of a Competence Models Creation Using the Ahp Method and Saaty’s Method of Determining Weights’, in Hradecke Ekonomicke Dny 2014: Ekonomicky Rozvoj a Management Regionu, Dil I, P. Jedlicka, Ed., Hradec Kralove: Gaudeamus, 2014, pp. 165–172. Accessed: Feb. 01, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/full-record/WOS:000398250000022
4. L. Tung and S. L. Tang, ‘A comparison of the Saaty’s AHP and modified AHP for right and left eigenvector inconsistency’, Eur. J. Oper. Res., vol. 106, no. 1, pp. 123–128, Apr. 1998, doi: 10.1016/S0377-2217(98)00353-1.
5. Coffey and D. Claudio, ‘In defense of group fuzzy AHP: A comparison of group fuzzy AHP and group AHP with confidence intervals’, Expert Syst. Appl., vol. 178, p. 114970, Sep. 2021, doi: 10.1016/j.eswa.2021.114970.
6. -J. Wang, ‘Interval-Valued Fuzzy Multi-Criteria Decision-Making by Combining Analytic Hierarchy Process with Utility Representation Function’, Int. J. Inf. Technol. Decis. Mak., vol. 21, no. 05, pp. 1433–1465, Sep. 2022, doi: 10.1142/S0219622022500225.
7. Miciuła, J. Nowakowska-Grunt, Using the AHP Method to Select an Energy Supplier for Household in Poland. Procedia Comput. Sci.2019, 159, 2324–2334.
8. Turk and M. Ozkok, ‘Shipyard location selection based on fuzzy AHP and TOPSIS’, J. Intell. Fuzzy Syst., vol. 39, no. 3, pp. 4557–4576, 2020, doi: 10.3233/JIFS-200522.
9. L. Saaty, Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. RWS Publications, 2012.
10. M. Vadivel, A. H. Sequeira, S. K. Jauhar, and V. Chandana, ‘Tamilnadu Omnibus Travels Evaluation Using TOPSIS and Fuzzy TOPSIS Methods’, in Innovations in Bio-Inspired Computing and Applications, Ibica 2021, A. Abraham, A. M. Madureira, A. Kaklauskas, N. Gandhi, A. Bajaj, A. K. Muda, D. Kriksciuniene, and J. C. Ferreira, Eds., Cham: Springer International Publishing Ag, 2022, pp. 24–31. doi: 10.1007/978-3-030-96299-9_3.
11. Kuttler, B. Cilali, and K. Barker, ‘Destination Selection in Environmental Migration with TOPSIS’, in 2021 Systems and Information Engineering Design Symposium (ieee Sieds 2021), New York: Ieee, 2021, pp. 261–266. Accessed: Mar. 05, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/full-record/WOS:000828133400047

 

Appendix A is mentioned, but is nowehere to be found - maybe it is an additional file that the reviewer doesn't have access to? 

Authors’ response: Within this section, we have included the description of the methodology that was in the appendix so that the methodology is thoroughly covered in one section. The methodology has been clarified and more precisely described. So from the appendix we added the description of the methodology to the article, and we included the details of the data in the appendix.

 

The contributions of the paper are unclear. Also, is this paper a review of existing models? Or does it imply any implementation? Please clarify. 

Authors’ response: We made the necessary corrections in paper. The energy sector faces numerous challenges, including the need for increased energy efficiency, cost reduction, and grid stability. To address these challenges, the Collaborative Energy Optimization Platform (CEOP) has emerged as a promising solution. This paper presents a comprehensive review of the Collaborative Energy Optimization Platform (CEOP), an innovative model that utilizes AI algorithms in an integrated manner. The review of the CEOP model is based on an in-depth analysis of existing literature, research papers, and industry reports. The methodology encompasses a systematic review of the model's key features, including collaboration, data-sharing, and AI algorithm integration. The successful implementation of the CEOP model requires careful consideration of various factors, such as regulatory frameworks, data privacy and security measures, and stakeholder engagement. Effective collaboration platforms, robust data infrastructure, and AI algorithm deployment are essential components of the implementation strategy. Furthermore, policymakers play a crucial role in creating an enabling environment that incentivizes stakeholders to actively participate in the CEOP model.

 

We have incorporated all the suggestions made by the reviewers.

Those changes are highlighted within the revised manuscript file with tracked changes.

Thank you for the positive reception of the article, the clear review and suggestions for corrections to improve our article.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear Authors

I congratulate you for the extensive revision you have implemented in the current version of the manuscript energies-2469218. I have seen that the observations reported by the reviewers in the first round of revision have been implemented. In the current version, I have not observed any other improvement that could be pointed out that would substantially increase the quality of the manuscript. I believe that it is in a condition to be considered for publication.

Best regards

 

Reviewer

Reviewer 2 Report

Improved the paper as per the suggestions. all the best to the authors

Reviewer 3 Report

All the observations were addressed