Electricity Demand Forecasting of Hospital Buildings in Istanbul
Sam C. M Hui
Round 1
Reviewer 1 Report
In this study, the annual electrical energy consumption of 23 public hospitals with over 100 beds in Istanbul is measured, and after determining the monthly peak loads, forecasting models are suggested using regression techniques for maximum demand forecasting.
ï‚· The general useability of the research outcome has to be highlighted for the following points
- Is the formula generalised for other countries?
- Has different typologies of hospitals need to be discussed, and a more detailed study should be conducted to generalise the results
- The author has mentioned various machine learning techniques used to accurately predict the electrical load in buildings; what is the justification for adopting linear models to predict the demand in the study?
- Limitations of the model chosen in capturing non-linear associations should be discussed
- Basic terminologies such as system capacity and its relation to calculating demand should be elaborated in the introduction through literature.
- The scope of the manuscript and its specific contribution to literature should be better highlighted at the end of the introduction. There is a lot of information there, but what is novel in relation to a large amount of literature on this subject should be better shown.
Author Response
Response to Reviewer 1 Comments
Point 1: Is the formula generalised for other countries?
Response 1: In the literature, there is information about the annual energy use intensity (EUI, kWh/m2) of hospitals in various countries. However, there is no information about the maximum demand of hospitals. When hospitals in developed countries are evaluated in terms of medical devices and electromechanical equipment, they are generally similar to each other. Since the formula calculates the system capacity, it is predicted that the method can be used in countries such as America, Germany, and England when it is evaluated that the devices using the system capacity and the health system are similar. However, data and further studies are needed to apply the results of the study all over the world.
Point 2: Has different typologies of hospitals need to be discussed, and a more detailed study should be conducted to generalise the results
Response 2: Hospital typology does not affect the results of the study. For example, while HVAC systems are used extensively in new hospitals, HVAC systems are used very little in old hospitals. Therefore, while the HVAC load is included in the installed power calculation in new hospitals, it is not included in the old hospitals. As a result, since the formula calculates the system capacity, it can estimate the maximum power with high accuracy. Therefore, the formula can be applied to all hospitals, regardless of the typology. Whether the hospital typology affects the results of the study is discussed in the discussion section, accompanied by sample architectural plans and visuals.
Point 3: The author has mentioned various machine learning techniques used to accurately predict the electrical load in buildings; what is the justification for adopting linear models to predict the demand in the study?
Response 3: Since the purpose of the study is to create a simple and practical method, the linear model was preferred. Within the scope of the study, the Neural Network model was also created in the Matlab program and a little better results were obtained than the linear model. For example, the installed power of Mehmet Akif Ersoy Cardiovascular Surgery Training and Research Hospital is 5175 kVA, and the maximum power was calculated at 1728 kVA when the linear model is applied, and the maximum power was calculated at 1720 kVA when the neural network model is applied. The actual maximum power is 1718 kVA. While the margin of error was 0.5% in the linear model for Bahçelievler State Hospital, the margin of error was 0.1% in the neural network model. Since the error percentage is low in both models, the linear model, which is a more simple and practical method, was preferred because the neural network model requires a program and a computer.
Point 4: Limitations of the model chosen in capturing non-linear associations should be discussed
Response 4: In the data obtained so far, has shown that there is a linear relationship between the installed power and the actual maximum power. If nonlinear relationships arise in the future, additional studies may be performed.
Point 5: Basic terminologies such as system capacity and its relation to calculating demand should be elaborated in the introduction through literature.
Response 5: It has been added to the introduction through the literature.
Point 6: The scope of the manuscript and its specific contribution to literature should be better highlighted at the end of the introduction. There is a lot of information there, but what is novel in relation to a large amount of literature on this subject should be better shown.
Response 6: The scope of the manuscript and its specific contribution to literature has been added at the end of the introduction.
Thank you for your comments.
Reviewer 2 Report
The research represents an interesting original study on electricity demand forecasting and can provide hints for further research and optimization of electrical system design for hospital buildings. Suggestions for revisions are given below.
1. Paper title should be revised to reflect better the contents. For example, a new title can be "Electricity Demand Forecasting of Hospital Buildings in Istanbul".
2. Keywords should be updated to indicate the key concepts of the research.
3. Section 2 should be renamed as "Hospital Data and Analysis Methods". Sub-section 2.1 should be renamed as "Hospital Data".
4. Sub-section 3.3 should be renamed as "Cost Saving Implications". It is recommended to also discuss the impact on the sustainability of electrical system design ad operation.
5. References: Should check the accuracy and consistency of the reference description. For example, Year should be put after the names of authors.
Author Response
Response to Reviewer 2 Comments
Point 1: Paper title should be revised to reflect better the contents. For example, a new title can be "Electricity Demand Forecasting of Hospital Buildings in Istanbul".
Response 1: The necessary corrections have been made.
Point 2: Keywords should be updated to indicate the key concepts of the research.
Response 2: The necessary corrections have been made.
Point 3: Section 2 should be renamed as "Hospital Data and Analysis Methods". Sub-section 2.1 should be renamed as "Hospital Data".
Response 3: The necessary corrections have been made.
Point 4: Sub-section 3.3 should be renamed as "Cost Saving Implications". It is recommended to also discuss the impact on the sustainability of electrical system design ad operation.
Response 4: The necessary corrections have been made.
Point 5: References: Should check the accuracy and consistency of the reference description. For example, Year should be put after the names of authors.
Response 5: The necessary corrections have been made.
Thank you for your comments.
Reviewer 3 Report
Reviewer Feedback
The topic is interesting, and it is noteworthy to investigate electrical power performance of representative hospital buildings in Turkey, but the paper is required amendments. I outlined my recommendations as follow.
1. Re-write Abstract to include knowledge gap, methods used and the key results that are the contribution to knowledge.
2. A nomenclature Table, including list of abbreviations should be given.
3. In Section 1 (Introduction), on page 2, in lines between 64 and 66, please update the citation here, I recommend to the look at the most-up-to-date pilot studies which primarily considered the energy performance of buildings by considering optimisation as part of their study. Please consider below indicated resources while you are revising the literature review to identify knowledge gap clearly.
• Ozarisoy, B., & Altan, H. (2021). A novel methodological framework for the optimisation of post-war social housing developments in the South-eastern Mediterranean climate: Policy design and life-cycle cost impact analysis of retrofitting strategies. Solar Energy, 225, 517–560. https://doi.org/10.1016/j.solener.2021.07.008
• Altan, H., & Ozarisoy, B. (2022). An Analysis of the Development of Modular Building Design Elements to Improve Thermal Performance of a Representative High Rise Residential Estate in the Coastline City of Famagusta, Cyprus. Sustainability, 14(7), 4065. https://doi.org/10.3390/su14074065
To increase the scientific soundness of the authors’ their own work, I recommend to the authors to cite above indicated resources properly.
4. Contribution to knowledge, novelty of the study and significance of the study should be indicated in the Introduction.
5. In Sub-section 2.1 (Materials) Discuss the choice and limitation of using 23 hospitals as case study to analyse the data. Alternatively, the authors should design the methodological flow diagram to increase the credibility of their work here.
6. In Sub-section 2.1 (Materials), With regards to the Figure 1, copyright permission is requested to include this source into the manuscript.
7. In Sub-section (Materials), the authors should include (i) the location map of each representative case study hospital to increase the visual quality of their paper; (ii) the real image or architectural render of each hospital should be included to understand the physical quality of each case study buildings. Please also explain why the authors chose particularly hospital buildings. I recommend to the authors to use the statistical data on the buildings stock in Turkey and demonstrates the representativeness of hospital buildings selected for the study.
8. In Sub-section 2.1.2 (Coincidence Factor). Present reasons for statistical evidence of the selection of hospital buildings being ‘nationally representative’. Explain how the sample is random and not just chance.
9. In Sub-section 2.1.2 (Coincidence Factor), in Table 3, How did the authors identify the representative occupancy profiles?
10. In Sub-section 2.2 (Methods), the reported parameters are unclear. Please justify them clearly. Alternatively, the authors could design a flow diagram to outline the step-by-step development of the process.
11. In Section 3 (Results), in Figure 2, the authors demonstrate the results of a linear regression analysis. The linear regression analysis is the superscript of this project, and I would like to see additional work on the outcomes of the linear regression analysis in Graphs and Tables. This could increase scientific soundness and credibility of this paper.
12. Discussions section should be added, and the findings should be incorporated with the literature review which helps the authors to increase both credibility and scientific soundness of their work. The results are then used to produce a benchmark. The paper should reflect this.
13. Limitations of the study should be outlined and supported with a visual diagram to demonstrate a roadmap to the scholars who are conducting their research work in line with the scope of the present study.
14. The conclusion should be kept succinct. Future work and impacts should be discussed.
Author Response
Response to Reviewer 3 Comments
Point 1: Re-write Abstract to include knowledge gap, methods used and the key results that are the contribution to knowledge.
Response 1: The necessary corrections have been made.
Point 2: A nomenclature Table, including list of abbreviations should be given.
Response 2: The necessary corrections have been made.
Point 3: In Section 1 (Introduction), on page 2, in lines between 64 and 66, please update the citation here,
I recommend to the look at the most-up-to-date pilot studies which primarily considered the energy performance of buildings by considering optimisation as part of their study. Please consider below indicated resources while you are revising the literature review to identify knowledge gap clearly.
- Ozarisoy, B., & Altan, H. (2021). A novel methodological framework for the optimisation of post-war social housing developments in the South-eastern Mediterranean climate: Policy design and life-cycle cost impact analysis of retrofitting strategies. Solar Energy, 225, 517–560. https://doi.org/10.1016/j.solener.2021.07.008
- Altan, H., & Ozarisoy, B. (2022). An Analysis of the Development of Modular Building Design Elements to Improve Thermal Performance of a Representative High Rise Residential Estate in the Coastline City of Famagusta, Cyprus. Sustainability, 14(7), 4065. https://doi.org/10.3390/su14074065
To increase the scientific soundness of the authors’ their own work, I recommend to the authors to cite above indicated resources properly.
Response 3: The necessary corrections have been made.
Point 4: Contribution to knowledge, novelty of the study and significance of the study should be indicated in the Introduction.
Response 4: The necessary corrections have been made.
Point 5: In Sub-section 2.1 (Materials) Discuss the choice and limitation of using 23 hospitals as case study to analyse the data. Alternatively, the authors should design the methodological flow diagram to increase the credibility of their work here.
Response 5: It has been added to the discussion Section.
Point 6: In Sub-section 2.1 (Materials), With regards to the Figure 1, copyright permission is requested to include this source into the manuscript.
Response 6: The necessary corrections have been made.
Point 7: In Sub-section (Materials), the authors should include (i) the location map of each representative case study hospital to increase the visual quality of their paper; (ii) the real image or architectural render of each hospital should be included to understand the physical quality of each case study buildings. Please also explain why the authors chose particularly hospital buildings. I recommend to the authors to use the statistical data on the buildings stock in Turkey and demonstrates the representativeness of hospital buildings selected for the study.
Response 7: It has been added to the manuscript.
Point 8: In Sub-section 2.1.2 (Coincidence Factor). Present reasons for statistical evidence of the selection of hospital buildings being ‘nationally representative’. Explain how the sample is random and not just chance.
Response 8: These values are the values from the regulation. The regulation is specified in the references section. The values were not found as a result of any coincidence. The name of the coefficient is the coincidence coefficient.
Point 9: In Sub-section 2.1.2 (Coincidence Factor), in Table 3, How did the authors identify the representative occupancy profiles?
Response 9: These values are the values from the regulation. The regulation is specified in the references section. The values were not found as a result of any coincidence. The name of the coefficient is the coincidence coefficient.
Point 10: In Sub-section 2.2 (Methods), the reported parameters are unclear. Please justify them clearly. Alternatively, the authors could design a flow diagram to outline the step-by-step development of the process.
Response 10: The necessary corrections have been made.
Point 11: In Section 3 (Results), in Figure 2, the authors demonstrate the results of a linear regression analysis. The linear regression analysis is the superscript of this project, and I would like to see additional work on the outcomes of the linear regression analysis in Graphs and Tables. This could increase scientific soundness and credibility of this paper.
Response 11: It has been added to the discussion Section.
Point 12: Discussions section should be added, and the findings should be incorporated with the literature review which helps the authors to increase both credibility and scientific soundness of their work. The results are then used to produce a benchmark. The paper should reflect this.
Response 12: The necessary corrections have been made.
Point 13: Limitations of the study should be outlined and supported with a visual diagram to demonstrate a roadmap to the scholars who are conducting their research work in line with the scope of the present study.
Response 13: The necessary corrections have been made.
Point 14: The conclusion should be kept succinct. Future work and impacts should be discussed.
Response 14: The necessary corrections have been made.
Thank you for your comments.
Reviewer 4 Report
As we know, at the stage of engineering design, it is very important and difficult to determine the power system capacity. The research of this paper is quite good, especially given a large number of practical cases, which can better support the application of engineering design.
Author Response
Thank you for your comments.
Round 2
Reviewer 1 Report
good
Reviewer 3 Report
The authors have been addressed all changes. Very well done.
