A Meta-Survey on Intelligent Energy-Efficient Buildings
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe paper provides an extensive meta-survey covering various machine-learning techniques used in Intelligent Energy-Efficient Buildings (IEEBs). This thorough examination helps understand the current state-of-the-art and identify future research directions. The paper effectively demonstrates the interdisciplinary nature of IEEBs, linking machine learning, IoT, and energy efficiency. This integration is crucial for developing holistic solutions significantly impacting energy consumption and sustainability.
There are some areas where improvements could enhance its impact and clarity:
1- Including detailed case studies or examples of successful IEEB implementations could provide practical insights and demonstrate the real-world applicability of the discussed methods. This would also illustrate the challenges and solutions in context, making the findings more tangible.
2- While challenges are mentioned, a deeper exploration of these issues and potential solutions or mitigation strategies would provide a more comprehensive understanding. Discussing how current research is addressing these challenges could also be valuable.
Comments on the Quality of English Language
While the paper is generally well-written, ensuring consistency in terminology and refining the structure for better flow can enhance readability. Breaking down long paragraphs and using more subheadings can help you navigate the content more easily.
Author Response
Please, consider the attached file.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Editor,
I am writing to you as a reviewer of the manuscript titled " A Meta Survey on Intelligent Energy Efficient Buildings" which was submitted to Big Data Cogn. Comput. with the manuscript ID 3050458.
The article is a meta-survey that aims to provide a comprehensive overview of the current state-of-the-art research in the field of Intelligent Energy Efficient Buildings (IEEBs). The authors analyzed 21 survey papers in the field and identified key trends, challenges, and research directions. I would rate this article as follows:
Content: 8/10 (The article provides a comprehensive overview of the current state-of-the-art research in IEEBs, but could benefit from more in-depth analysis and discussion of the findings.)
Organization: 7/10 (The article is well-structured, but some sections could be more clearly defined and separated.)
Writing: 8/10 (The writing is clear and concise, but could benefit from more varied sentence structures and vocabulary.)
Depth of analysis: 6/10 (The article provides a broad overview of the field, but could benefit from more in-depth analysis of specific topics and findings.)
Originality: 5/10 (The article is a meta-survey that summarizes existing research, rather than presenting new or original findings.)
Overall, I would rate this article as 7.5/10. It provides a useful overview of the current state-of-the-art research in IEEBs but could benefit from more in-depth analysis and discussion of the findings. Some advice and suggestions are here:
- Abstract:
Advice:
1. The abstract starts by mentioning the rise of IoT and its applications, but it doesn't clearly state what the main contribution of the paper is. What is the novel aspect of the research? What does the paper aim to achieve?
2. The abstract mentions that the paper presents a systematic meta-survey, but it doesn't specify what this means or what aspects of IEEBs are being reviewed. What specific subfields of ML are being compared?
3. The abstract mentions that the paper provides valuable insights and aids decision-making processes but doesn't explain how it will impact the field.
Suggestion:
1. Clearly state the paper's main contribution.
2. Provide more details about the scope and focus of the meta-survey.
3. Explain why the insights gained from this meta-survey are important or groundbreaking.
- Introduction:
Advice:
1. The introduction jumps abruptly from discussing ML applications to mentioning the importance of IEEBs. This makes it hard to follow.
Suggestion:
1. Use a clear structure to organize the introduction. For example, you could start by introducing the main topic, then provide a brief overview of the significance of IEEBs, and finally discuss the scope of the paper.
- A Brief Background Discussion:
Advice:
1. Use headings and subheadings to summarize the text and provide a clear structure. This will help readers navigate the content more easily.
2. Use visual aids such as diagrams, charts, and graphs to help illustrate complex concepts and make the text more engaging.
Suggestion:
1. Divide the text into smaller sections or paragraphs that focus on specific topics, such as:
* Introduction to Machine Learning (ML) and its applications in IEEBs
* Overview of supervised, unsupervised, semi-supervised, and self-supervised learning
* Description of specific ML algorithms used in IEEBs (e.g., SVM, ANN, DNN, etc.)
2. Use concrete examples and illustrations to help illustrate complex concepts and make them more relatable. This will help readers understand the material better.
- Survey Strategies: Intelligent Energy Efficient Buildings:
Advice:
Provide access to all data and materials used in the study.
- Literature Review: The Intelligent Energy-Efficient Buildings:
Advice:
1. The review focuses too much on summarizing the results of each paper, rather than providing analysis and insights.
2. The review concludes abruptly without discussing the implications of the findings or potential future research directions.
Suggestion:
1. Highlight the strengths and limitations of each study and provide a critical evaluation of the findings.
2. Identify potential areas for future research and suggest potential research questions or hypotheses.
Conclusion:
Advice:
The problem with the conclusion is that it is too brief. It also does not provide a clear direction for future research or a specific call to action.
Suggestion:
1. Start by summarizing the main findings, such as the trends, challenges, and research directions in IEEBs.
2. Highlight the most important results: Identify the most significant findings and highlight them in your conclusion.
3. Explain how your findings can be applied to real-world scenarios, and how they can contribute to the development of more intelligent, energy-efficient, and sustainable IEEBs.
Ultimately, this article may be accepted after the requisite revisions and reassessment.
Thank you for considering my opinion.
Sincerely,
Author Response
Please, consider the attached file.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors-It would benefit the readership if more information was included on:
a. RQ3 the Deep Learning methods as well
b. RQ5 the SVM, ANN and ensemble learning (p13 ,line480)
-the acronym HVAC should be defined before used (p.1, line 30)
Author Response
Please, consider the attached file.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsDear Authors,
in the IoT era more and more devices are interconnected and conected to the Internet. The sutainability goals and extensive use of renewable energy sources lead to development of IEEBs and smart grids amon othes things.
The paper you presented is interesting as it provides an overview of different ML approaches in smart buildings.
However, some things could be written in more detail.
I suggest writting more on different sensors/actuators used in IEEBs, how the communicate, introduce communication protocols, provide information on the IoT architecture so that the reader is aware of the amount of data generated in a smart building and where ML is applied.
You could also elaborate on specific application of ML in IEEBs, give examples on energy efficiency levels (if possible).
Regards,
Reviewer
Author Response
Please, consider the attached file.
Author Response File: Author Response.pdf
