The Impact of Detail, Shadowing and Thermal Zoning Levels on Urban Building Energy Modelling (UBEM) on a District Scaleâ€
Round 1
Reviewer 1 Report
This study conducts an analysis of the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance. The paper is well written and well structured. However, there are some minor issues as listed below needed to be addressed before the paper can be accepted.
- There is a lack of literature review on city-scale energy modeling of buildings. It would be better to add some review on top-down modelling and bottom-up modelling.
- The significance of the research has not been pointed out clearly.
- For the case study, the average performance for Minneberg is 76 kWh/m2 and the average performance for Hammarby Sjöstad is 114 kWh/m2. Where did you get those data? Any references or resources?
Author Response
Reviewer 1
This study conducts an analysis of the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance. The paper is well written and well structured. However, there are some minor issues as listed below needed to be addressed before the paper can be accepted.
- There is a lack of literature review on city-scale energy modeling of buildings. It would be better to add some review on top-down modelling and bottom-up modelling.
- The significance of the research has not been pointed out clearly.
- For the case study, the average performance for Minneberg is 76 kWh/m2 and the average performance for Hammarby Sjöstad is 114 kWh/m2. Where did you get those data? Any references or resources?
Response
The authors are thankful to the reviewer for positive assessment and constructive feedback that allowed to improve the manuscript.
Q1. There is a lack of literature review on city-scale energy modeling of buildings. It would be better to add some review on top-down modelling and bottom-up modelling.
The review on UBEMs was extended in the introduction section as follows:
…
The initial uptake of city-scale building energy modelling was captured in the reviews by Swan and Ugursal [4] and Kavgic [5] that provided categorisation of the models into top-down and bottom-up, where later were divided into statistical and engineering. Top-down approach imposes the representation of the entire building stock as a single unit of analysis. Contrary, the bottom-up approach intends to focus on individual buildings. In their turn, statistical and engineering stand for data-driven or physics-based models being later joined by hybrid reduced-order models combining both approaches. The subsequent review by Reinhart and Davila [6] introduced the term of ‘Urban Building Energy Modelling (UBEM) attributed explicitly to bottom-up engineering models. […] Most of recent review papers tend to focus on these type of models as UBEMs, systematising their functional components [7], applied approaches [8] and key challenges [9]. However, a recent review by Ali et al. [10] gets back to the wider scope providing a comparative analysis of modern top-down and bottom-up urban-scale energy models.
A number of UBEM environments and tools have been developed in recent decade [11]. These include UBEMs using more detailed physics-based thermal engine such as EnergyPlus (CityBES [12], UMI [13]), simpler reduced-order models based on self-made RC networks (DIMOSIM [14], CitySim [15] or not formally named [16]), energy signatures [17] or the ISO/CEN standard method (SimStadt [18], CEA [19]). The review of UBEM cases in [20] shows that the choice of the model can be attributed to the project constraints, data and skills’ availability, and, ultimately, the purpose of developed UBEM.
…
Q2. The significance of the research has not been pointed out clearly.
Thank you for bringing attention to this deficiency. The significance of the research was articulated in the respective parts in the Introduction and Conclusion:
[Introduction]
… Hence, the main value of the proposed study is in characterizing the impact of these implicit assumptions on the quality of UBEMs. This contribution is expected to raise awareness of scholars and practitioners, provide more ground-based reason for making these modelling choices and, finally, improve the quality of decision-making based on these promising and powerful modelling tools.
…
[Conclusion]
We conclude that the analysed modeller assumptions embedded in UBEMs have a distinct impact on the UBEMs’ outcome and suggest promoting more explicit documentation of these choices in upcoming UBEM studies.
Q3. For the case study, the average performance for Minneberg is 76 kWh/m2 and the average performance for Hammarby Sjöstad is 114 kWh/m2. Where did you get those data? Any references or resources?
The authors thank the reviewer for bringing up this issue. The corresponding clarification footnote was added in the Case study section:
…over the 33 buildings1, the average performance according to EPCs is 76 kWh/m2, with a standard deviation of 10 kWh/m2…
1 Hereafter, buildings with the currently valid (less than 10 year old) EPCs are considered.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The manuscript describes the flow chart and computation methods used in the newly developed Urban Building Energy Modelling (UBEM) simulation tool, called Massive Urban Building Energy Simulations (MUBES). Then, the tool was used for simulation of 2 urban areas in Stockholm. Using those results the effect of the 3 important modelling choices: 1) the level of detail of buildings’ geometry, 2) thermal zoning assumed, and 3) the surrounding shadowing environment, on the simulation results accuracy has been analysed.
The manuscript is very well written, deals with a very important issue and fits very well to the aims of the journal. The only pioint which could be (and should be, in Reviewer's opinion) improved is introduction, where other UBEM softwares could be directly mentioned and cited. This would make the manuscript more self-contained and useful for the Reader.
After introducing the above mentioned minor correction, the manuscript might be accepted for publication.
Author Response
Reviewer 2
The manuscript describes the flow chart and computation methods used in the newly developed Urban Building Energy Modelling (UBEM) simulation tool, called Massive Urban Building Energy Simulations (MUBES). Then, the tool was used for simulation of 2 urban areas in Stockholm. Using those results the effect of the 3 important modelling choices: 1) the level of detail of buildings’ geometry, 2) thermal zoning assumed, and 3) the surrounding shadowing environment, on the simulation results accuracy has been analysed.
The manuscript is very well written, deals with a very important issue and fits very well to the aims of the journal. The only pioint which could be (and should be, in Reviewer's opinion) improved is introduction, where other UBEM softwares could be directly mentioned and cited. This would make the manuscript more self-contained and useful for the Reader.
After introducing the above mentioned minor correction, the manuscript might be accepted for publication.
Response
The authors would like to thank the reviewer for this very positive feedback. We followed your piece of advice and the review of UBEM models and tools in Introduction as follows:
The review on UBEMs was extended in the introduction section as follows:
…
The initial uptake of city-scale building energy modelling was captured in the reviews by Swan and Ugursal [4] and Kavgic [5] that provided categorisation of the models into top-down and bottom-up, where later were divided into statistical and engineering. Top-down approach imposes the representation of the entire building stock as a single unit of analysis. Contrary, the bottom-up approach intends to focus on individual buildings. In their turn, statistical and engineering stand for data-driven or physics-based models being later joined by hybrid reduced-order models combining both approaches. The subsequent review by Reinhart and Davila [6] introduced the term of ‘Urban Building Energy Modelling (UBEM) attributed explicitly to bottom-up engineering models. […] Most of recent review papers tend to focus on these type of models as UBEMs, systematising their functional components [7], applied approaches [8] and key challenges [9]. However, a recent review by Ali et al. [10] gets back to the wider scope providing a comparative analysis of modern top-down and bottom-up urban-scale energy models.
A number of UBEM environments and tools have been developed in recent decade [11]. These include UBEMs using more detailed physics-based thermal engine such as EnergyPlus (CityBES [12], UMI [13]), simpler reduced-order models based on self-made RC networks (DIMOSIM [14], CitySim [15] or not formally named [16]), energy signatures [17] or the ISO/CEN standard method (SimStadt [18], CEA [19]). The review of UBEM cases in [20] shows that the choice of the model can be attributed to the project constraints, data and skills’ availability, and, ultimately, the purpose of developed UBEM.
…
Author Response File: Author Response.docx
