A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation
Abstract
:1. Introduction
2. Materials and Methods
- Training phase (gray box): SD-GAN is divided into two sub-models. By learning from samples, Model 1 can generate FSL from BEP, while FSL-to-BFP translation is possible with Model 2. A refined SD-GAN network could be obtained after this phase.
- Generation phase (green box): after SD-GAN has been trained and qualified, the BEPs of three existing cases are fed into SD-GAN to generate the optimized BFPs.
- Simulation and evaluation phase (blue box): the energy consumption of the existing cases and the generated schemes are modeled for simulating separately by DesignBuilder, and the results are compared.
2.1. Deep Learning Network
2.1.1. Network Architecture
2.1.2. Network Training and Testing
- The processed training set is fed into SD-GAN for the training step. Model 1 takes BEP as input to output colored FSL. Model 2 takes FSL as input and produces BFP. The output will become closer to the real data as the generator and discriminator evolve simultaneously.
- The test set is used to test the capability of SD-GAN once the generator and discriminator have converged to an equilibrium state. The BEP of the test set can be input, and then the output generated results can be visually compared to the original image.
- The quantitative scoring method is used for the outcome assessment step. The generated results of Model 1 are evaluated and scored from the clarity of space allocation (CSA), the rationality of function distribution (RFD), and the clarity of color-block boundary (CCB) in turn (unacceptable: 0; bad: 1; not bad: 2; acceptable: 3; good: 4; very good: 5). The generated results of Model 2 are evaluated and scored from the wall-generated accuracy (WGA) and furniture-generated accuracy (FGA) in turn (unacceptable: 0; bad: 1; not bad: 2; acceptable: 3; good: 4; very good: 5).
2.2. Data Set Arrangement
2.2.1. Data Screening
- Entries with two or more floors were screened out to make the sample processing easier, and only those with a single floor were retained.
- Some entries were designed with variable space to improve space utilization. Entries with flexible variable space and extremely flexible functional layouts were screened out since this type of space cannot accurately define the functional zoning attributes.
2.2.2. Data Processing
- Uniform drawings: Redraw each entry’s architectural plans and unify the overall furniture style, doors, and windows of the drawings. Each functional space was characterized by specific furniture.
- Uniform annotation: The screened entries had a relatively similar functional layout, with a living room, dining area, kitchen, one or two bedrooms, study or workspace, bathrooms, and equipment rooms. According to the annotation principle (Figure 4), the FSL corresponding to the floor plan of each entry was first created. Then, the building area was filled with black to generate the BEP, as shown in Figure 5.
- Uniform labeling: Each individual image in the label has a size range of 256 × 256 pixels, and the label’s canvas size is 90 mm × 180 mm with a resolution of 72 ppi. As shown in Figure 6, this study requires two separate labels: one with FSL and BEP placed on the left and right sides and the other with BFP and FSL placed on the left and right sides.
2.2.3. Data Augmentation
2.3. Evaluation of the Approach: Case Study and Simulation
2.3.1. Case Background
2.3.2. Building Energy Consumption Simulation Based on DesignBuilder
3. Results and Implementations
3.1. Preliminary Training
3.2. Data Augmentation Testing
3.3. SD-GAN Implementation and Case Study
4. Discussion
4.1. Generative Design and Efficiency
4.2. SD-GAN Training
- With the same network architecture, Model 2 performed remarkably better than Model 1, partly because the graphical features that correspond to FSL and BFP are more consistent. The network could easily perceive the correspondence between color boundaries and partition walls. On the other hand, the furniture varies more in different rooms. This implies that the network can quickly understand the mapping relationship between color and furniture arrangement. In contrast, the BEP-to-FSL mapping relationship of the Model 1 input is very ambiguous. The network may only be able to suspect from the proportional relationship of contours, orientation, etc. Hence, the learning ability performed weakly.
- There was rarely much difference between the learning performance of the 240 and the initial 90 samples after data augmentation. However, the learning ability of the 400 samples improved dramatically. This is probably a case of the network becoming stuck in a “local optimal solution” during the learning process. More samples help it to jump out of that optimal solution and solve faster to the global optimum. This also explains why Model 1-2 performed better than Model 1 with 400 epochs after only 200 epochs, improving the solving ability and saving a lot of repetitive and invalid learning.
5. Conclusions
- Trained an integrated RBFP generation network SD-GAN with energy-effective performance based on Pix2Pix with SD competition entries as sample set.
- SD-GAN with two steps of model configuration is capable of generating reasonable spatial and functional floor plans for single-floor residential buildings.
- SD-GAN trained with embedded energy-efficiency characteristics samples and also has the capacity to generate energy-saving RBFPs.
- Compared with three actual buildings by using DesignBuilder simulation, the RBFP generation network showed a positive effect in both optimizing the function arrangement and reducing energy consumption.
- Proper data augmentation method can significantly improve the network’s training results with small size sample.
- The screened entries with small shape differences may potentially limit the generative capacity of the network. The variability of the sample data should be expanded to enhance the generative possibility.
- Model training for multiple climate zones was not performed due to the limited data. The applicability under various climatic conditions will be investigated based on more samples.
- In design practice, the BEP, or building form, is an emerged result of complicated surroundings. Rather than being given, the complex external environment (road conditions, topographic features, surrounding business, urban context, etc.) may be considered in the future to generate BEP.
- The energy-saving design of a house is a complex process, including the design of various passive and active energy-saving strategies. In this study, SD-GAN has been experimentally demonstrated to learn latent SAP-solving strategies, resulting in certain passive energy-saving properties. In the future, it is necessary to build a more comprehensive model of an energy-effective generation network from the perspective of a 3D scheme, integrating both passive and active strategies.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Competition | Location | Entries | Retained |
---|---|---|---|
SD2007 | Washington, DC, USA | 20 | 15 |
SD2009 | Washington, DC, USA | 21 | 14 |
SD2011 | Washington, DC, USA | 19 | 13 |
SD2013 | Irvine, CA, USA | 19 | 16 |
SD2015 | Irvine, CA, USA | 15 | 11 |
SD2017 | Irvine, CA, USA | 11 | 9 |
SDE2010 | Madrid, Spain | 17 | 5 |
SDE2012 | Madrid, Spain | 18 | 8 |
SDEM2018 | Dubai, UAE | 14 | 7 |
Total | - | 154 | 98 |
Construction Method | Film Coefficient (W/m2K) | |
---|---|---|
External Wall | 370 mm clay brick + 20 mm cement | 1.54 |
External Window | aluminum framed glazing | 6.18 |
Roof | 100 mm concrete + 40 mm cement | 1.86 |
No. | Model 1 | No. | Model 2 | ||||
---|---|---|---|---|---|---|---|
Input | Output | Ground Truth | Input | Output | Ground Truth | ||
07-06 | 07-01 | ||||||
07-09 | 12-03 | ||||||
09-02 | 13-11 | ||||||
09-14 | 15-08 | ||||||
09-15 | 09-15 | ||||||
12-04 | 17-06 | ||||||
12-14 | 12-14 | ||||||
18-01 | 18-01 |
No. | Model 1 | No. | Model 2 | |||
---|---|---|---|---|---|---|
CSA | RFD | CCB | WGA | FGA | ||
07-06 | 3 | 2 | 2 | 07-01 | 5 | 4 |
07-09 | 4 | 3 | 3 | 12-03 | 5 | 5 |
09-02 | 1 | 1 | 2 | 13-11 | 5 | 4 |
09-14 | 4 | 3 | 3 | 15-08 | 4 | 5 |
09-15 | 1 | 1 | 2 | 09-15 | 5 | 4 |
12-04 | 1 | 1 | 1 | 17-06 | 5 | 4 |
12-14 | 2 | 2 | 2 | 12-14 | 5 | 4 |
18-01 | 2 | 2 | 3 | 18-01 | 5 | 5 |
Average | 2.25 | 1.875 | 2.25 | Average | 4.875 | 4.375 |
No. | Model 1 | Model 1-1 | Model 1-2 |
---|---|---|---|
07-06 | |||
07-09 | |||
09-02 | |||
09-14 | |||
09-15 | |||
12-04 | |||
12-14 | |||
18-01 |
No. | Model 1-1 | Model 1-2 | ||||
---|---|---|---|---|---|---|
CSA | RFD | CCB | CSA | RFD | CCB | |
07-06 | 1 | 1 | 1 | 5 | 4 | 5 |
07-09 | 3 | 2 | 4 | 4 | 5 | 4 |
09-02 | 2 | 2 | 2 | 5 | 5 | 4 |
09-14 | 1 | 2 | 3 | 5 | 5 | 4 |
09-15 | 3 | 2 | 3 | 5 | 5 | 5 |
12-04 | 1 | 1 | 2 | 5 | 5 | 4 |
12-14 | 4 | 2 | 4 | 5 | 5 | 4 |
18-01 | 3 | 2 | 3 | 5 | 3 | 4 |
Average | 2.25 | 1.75 | 2.75 | 4.875 | 4.625 | 4.25 |
Item | Case A | Case B | Case C | |
---|---|---|---|---|
FSLs generated | ||||
BFPs generated | ||||
DesignBuilder Model | ||||
Annual Energy Consumption (kWh) | Heating (existing) | 11,375.03 | 15,350.27 | 11,488.38 |
Heating (generated) | 9578.11 | 12,766.55 | 10,407.00 | |
Cooling (existing) | 2276.10 | 2701.41 | 1969.11 | |
Cooling (generated) | 2055.39 | 2364.34 | 1968.44 | |
Total (existing) | 16,380.14 | 20,608.09 | 15,686.90 | |
Total (generated) | 14,188.26 | 17,982.44 | 14,515.83 |
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Wan, D.; Zhao, X.; Lu, W.; Li, P.; Shi, X.; Fukuda, H. A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation. Sustainability 2022, 14, 8074. https://doi.org/10.3390/su14138074
Wan D, Zhao X, Lu W, Li P, Shi X, Fukuda H. A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation. Sustainability. 2022; 14(13):8074. https://doi.org/10.3390/su14138074
Chicago/Turabian StyleWan, Da, Xiaoyu Zhao, Wanmei Lu, Pengbo Li, Xinyu Shi, and Hiroatsu Fukuda. 2022. "A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation" Sustainability 14, no. 13: 8074. https://doi.org/10.3390/su14138074