Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages
Abstract
:Featured Application
Abstract
1. Introduction and Research Scope
2. Literature Review
2.1. Energy Simulation Methods: Top-Down Approaches
2.2. Energy Simulation Methods: Bottom-Up Approaches
3. Research Scope in Relation to the Literature
3.1. The Gap
3.2. The Objective of the Research
- Running a very large amount of energy simulations of archetype buildings by varying a chosen set of their characteristics;
- Collecting all the energy simulation results into a single database;
- Using machine learning techniques to synthesize the database developed into the forecasting tools by means of ANNs.
4. Methodological Approach
4.1. ANN Target Characteristics
- Instantaneously recalculate the main seasonal energy needs when the user modifies the input parameters, such as overall sizes, window ratios and building constructions, via sliders in a graphical user interface (GUI);
- Adapt to various building occupation levels.
4.2. The Database to Train the Networks
- -
- Heating energy needs: 7/250 kWh/m2;
- -
- Cooling energy needs: 3/90 kWh/m2;
- -
- Domestic hot water preparation energy needs: 3/40 kWh/m2;
- -
- Lighting energy needs: 1/15 kWh/m2;
- -
- Other electrical appliances energy needs: 7/50 kWh/m2.
- Heating energy demand (kWh/y);
- Cooling energy demand (kWh/y);
- Lighting energy demand (kWh/y);
- Electrical equipment energy demand (kWh/y);
- Domestic hot water (DHW) energy demand (kWh/y);
- Total solar energy transmitted by the windows’ facing, with no regard to the building’s azimuth angle (kWh/(m2·y)):
- -
- North;
- -
- East;
- -
- South;
- -
- West;
- Yearly average value of illuminance in the center of the zone during occupancy hours (lux);
- Yearly average value of CO2 concentration in the zone during occupancy hours (ppm);
- Average zone air temperature in the period of December–January (i.e., in midwinter) during occupancy hours (°C);
- Average zone air relative humidity in the period of December–January during occupancy hours (%);
- Average zone air temperature in the period of June–July (i.e., in midsummer) during occupancy hours (°C);
- Average zone air relative humidity in the period of June–July during occupancy hours (%);
- Calculated design heating capacity (kW);
- Calculated design cooling capacity (kW).
4.3. Producing the Training Database
4.4. ANN Training and Accuracy Improvement
5. Results
5.1. The Developed Software
5.2. An Example of the Simulation Results
5.3. Identify the Best DFANN Complexity
- Number of hidden layers (Min/Max): 2/6;
- Number of nodes per layer (Min/Max): 60/280;
- Maximum number of epochs: 1000, saving the best model developed along the epochs;
- Number of DFANNs concurring with the ensemble model: 10.
6. Discussion and Further Improvements
6.1. Calculation Time
6.2. A Test on Illustrative Case Studies
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Nomenclature
ANN | Artificial neural network |
API | Application programming interface |
BEM | Building energy modeling |
BP | Backpropagation |
DFANN | Deep feedforward artificial neural network |
GUI | Graphical user interface |
HVAC | Heating, ventilation and air-conditioning |
IAQ | Indoor air quality |
Idf | (EnergyPlus) input data file |
PV | Photovoltaics |
RNN | Recurrent neural network |
ZEB | Zero-energy building |
References
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Code | Description | Unit | Range of Values | Notes | |
---|---|---|---|---|---|
Geometry and Layout | X | Length of side 1 of the floor | m | Minimum value: 7 Maximum value: 30 | |
Y | Length of side 2 of the floor | m | Minimum value: 7 Maximum value: 30 | ||
nStoreys | Number of stories | - | Minimum value: 1 Maximum value: 8 | ||
α | Building’s azimuth | - | Minimum value: 0 Maximum value: 90 | ||
FW,A,N | Window area fraction along north side (@ Building azimuth = 0°) | - | Minimum value: 0.01 Maximum value: 0.90 | ||
FW,A,E | Window area fraction along east side (@ Building azimuth = 0°) | - | Minimum value: 0.01 Maximum value: 0.90 | ||
FW,A,S | Window area fraction along south side (@ Building azimuth = 0°) | - | Minimum value: 0.01 Maximum value: 0.90 | ||
FW,A,W | Window area fraction along west side (@ Building azimuth = 0°) | - | Minimum value: 0.01 Maximum value: 0.90 | ||
Constructions | EWTh2 | External wall construction: thickness of the insulation layer | m | Minimum value: 0.00 Maximum value: 0.20 | For further details, please see Table 2. |
EWTh3 | External wall construction: thickness of the masonry or concrete layer | m | Minimum value: 0.20 Maximum value: 0.40 | ||
EWTC3 | External wall construction: thermal conductivity of the masonry or concrete layer | W/(m·K) | Minimum value: 0.60 Maximum value: 1.80 | ||
RTh2 | Roof construction: thickness of the insulation layer | m | Minimum value: 0.00 Maximum value: 0.20 | ||
FTh2 | Floor construction: thickness of the insulation layer | m | Minimum value: 0.00 Maximum value: 0.20 | ||
G | Glazing type | String | G1, G2, G2Le, G3Le | For further details, please see Table 3. | |
AII | Average infiltration flow rate intensity | ach | Minimum value: 0.01 Maximum value: 1.00 | ||
Occupancy-Related Data | PD | People density | np/m2 | Minimum value: 0.02 Maximum value: 0.08 | For further details, please see Table 5. |
LD | Density of electric consumption due to lights | W/m2 | Minimum value: 0 Maximum value: 6 | ||
ED | Density of electric consumption due to electric equipment | W/m2 | Minimum value: 0 Maximum value: 20 | ||
DHWI | Domestic hot water intensity | W/p | Minimum value: 0 Maximum value: 200 | ||
AVI | Average ventilation flow rate intensity | m³/(s·p) | Minimum value: 0.001 Maximum value: 0.010 | - |
Construction | Layer (-) | Thickness (m) | Thermal Conductivity (W/(m·K)) | Density (kg/m³) | Specific Heat (J/(kg·K)) | Thermal Resistance (m2·K/W) | Total Thermal Conductance (W/(m2·K)) |
---|---|---|---|---|---|---|---|
External wall | 01 (Ext) | 0.015 | 0.55 | 1500 | 900 | 0.027 | 0.15–6.04 |
02 | 0.000–0.200 | 0.034 | 45 | 900 | 0.000–5.882 | ||
03 | 0.200–0.400 | 0.600–1.800 | 1500 | 900 | 0.111–0.667 | ||
04 (Int) | 0.015 | 0.55 | 1500 | 900 | 0.027 | ||
Roof | 01 (Ext) | 0.02 | 0.4 | 700 | 2500 | 0.050 | 0.15–1.28 |
02 | 0.000–0.200 | 0.034 | 45 | 900 | 0.000–5.882 | ||
03 | - | - | - | - | 0.16 | ||
04 | 0.3 | 0.55 | 1500 | 900 | 0.545 | ||
05 (Int) | 0.015 | 0.55 | 1500 | 900 | 0.027 | ||
Floor | 01 (Ext) | 0.3 | 1.6 | 1800 | 900 | 0.188 | 0.16–3.83 |
02 | 0.000–0.200 | 0.034 | 100 | 900 | 0.000–5.882 | ||
03 | 0.1 | 1.6 | 1800 | 900 | 0.063 | ||
04 (Int) | 0.02 | 1.8 | 1800 | 900 | 0.011 |
Code (Alpha) | Description (Alpha) | U-Value (W/(m2·K)) | Visible Transmittance (-) | Solar Heat Gain Coefficient (-) | |
---|---|---|---|---|---|
Glazing types | G1 | Single glazing | 5.70 | 0.86 | 0.90 |
G2 | Double glazing | 2.70 | 0.76 | 0.81 | |
G2Le | Double glazing, argon-filled, low emissivity film for solar gain | 1.10 | 0.63 | 0.72 | |
G3Le | Triple glazing, argon-filled, low emissivity film for solar gain | 0.70 | 0.35 | 0.58 |
Scope | Weekdays | All Other Days | ||
---|---|---|---|---|
Time Period | Control Temperature (°C) | Time Period | Control Temperature (°C) | |
Heating | 00:00–08:00 | 16.0 | 00:00–08:00 | 16.0 |
08:00–24:00 | 20.0 | 08:00–24:00 | 20.0 | |
Cooling | 00:00–08:00 | 30.0 | 00:00–08:00 | 30.0 |
08:00–24:00 | 28.0 | 08:00–24:00 | 28.0 |
Category | Weekdays | All Other Days | ||
---|---|---|---|---|
Time | Percentage | Time | Percentage | |
People | 00:00–08:00 | 100% | 00:00–14:00 | 100% |
08:00–18:00 | 33% | 14:00–22:00 | 33% | |
18:00–24:00 | 100% | 22:00–24:00 | 100% | |
Lights | 00:00–08:00 | 0% | 00:00–14:00 | 0% |
08:00–18:00 | 0% | 14:00–18:00 | 100% | |
18:00–24:00 | 100% | 18:00–24:00 | 100% | |
Electric equipment | 00:00–08:00 | 20% | 00:00–14:00 | 20% |
08:00–18:00 | 30% | 14:00–22:00 | 100% | |
18:00–24:00 | 100% | 22:00–24:00 | 100% | |
Domestic hot water | 00:00–08:00 | 0% | 00:00–14:00 | 0% |
08:00–18:00 | 20% | 14:00–22:00 | 20% | |
18:00–24:00 | 100% | 22:00–24:00 | 100% |
Model ID [-] | Hidden Layers [-] | Nodes Per Layer [-] | Train Loss [-] | Test Loss [-] | Best Epoch [-] | Training Time [s] |
---|---|---|---|---|---|---|
1 | 2 | 140 | 2.81 × 10−5 | 2.85 × 10−5 | 992 | 25,285 |
2 | 2 | 280 | 2.25 × 10−5 | 2.35 × 10−5 | 997 | 29,470 |
3 | 3 | 140 | 2.01 × 10−5 | 2.03 × 10−5 | 992 | 28,062 |
4 | 3 | 280 | 1.66 × 10−5 | 1.82 × 10−5 | 995 | 34,723 |
5 | 4 | 140 | 1.83 × 10−5 | 1.89 × 10−5 | 997 | 30,525 |
6 | 4 | 280 | 1.40 × 10−5 | 1.54 × 10−5 | 999 | 39,641 |
7 | 5 | 140 | 1.67 × 10−5 | 1.73 × 10−5 | 995 | 33,096 |
8 | 5 | 280 | 1.31 × 10−5 | 1.51 × 10−5 | 984 | 43,685 |
9 | 6 | 140 | 1.55 × 10−5 | 1.63 × 10−5 | 993 | 34,988 |
10 | 6 | 280 | 1.23 × 10−5 | 1.50 × 10−5 | 996 | 50,064 |
Relative Error (%) | Share of Training Samples (%) | Share of Test Samples (%) |
---|---|---|
0–5 | 94.2747 | 93.7016 |
5–10 | 4.9558 | 5.4067 |
10–20 | 0.7379 | 0.8515 |
20–50 | 0.0315 | 0.0396 |
50+ | 0.0000 | 0.0007 |
Relative Error (%) | Share of Training Samples (%) | Share of Test Samples (%) |
---|---|---|
0–5 | 98.4793 | 92.9048 |
5–10 | 1.3417 | 5.9539 |
10–20 | 0.1585 | 1.0656 |
20–50 | 0.0205 | 0.0757 |
50+ | 0.0000 | 0.0000 |
Relative Error (%) | Share of Test Samples (%) |
---|---|
0–5 | 99.5009 |
5–10 | 0.3958 |
10–20 | 0.0982 |
20–50 | 0.0051 |
50+ | 0.0000 |
Number of Predictions in the Batch (-) | 1 | 10 | 100 | 1000 |
---|---|---|---|---|
Time of Execution (s) | 0.023 | 0.026 | 0.033 | 0.123 |
Code | Zone | Floor Area | North Façade | East Façade | South Façade | West Façade | Heating Energy Consumption (by ANN) | Heating Energy Consumption (Simulated) | Error | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Total | Windows | Total | Windows | Total | Windows | Total | Windows | ||||||
m2 | m2 | m2 | m2 | m2 | m2 | m2 | m2 | m2 | kWh/m2-y | kWh/m2-y | % | ||
Building a | Padua | 144 | 69 | 25 | 40 | 0 | 69 | 14 | 40 | 0 | 14.4 | 13.90 | 3.60% |
Building b | Padua | 110 | 51 | 0 | 35 | 5 | 51 | 0 | 35 | 4 | 12.2 | 13.1 | −6.87% |
Building c | Vicenza | 127 | 30 | 7 | 40 | 8 | 25 | 0 | 43 | 4 | 16.8 | 16.1 | 4.35% |
Building d | Ravenna | 84 | 22 | 2 | 41 | 6 | 22 | 7 | 41 | 0 | 18.1 | 17.8 | 1.69% |
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Pittarello, M.; Scarpa, M.; Ruggeri, A.G.; Gabrielli, L.; Schibuola, L. Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages. Appl. Sci. 2021, 11, 5377. https://doi.org/10.3390/app11125377
Pittarello M, Scarpa M, Ruggeri AG, Gabrielli L, Schibuola L. Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages. Applied Sciences. 2021; 11(12):5377. https://doi.org/10.3390/app11125377
Chicago/Turabian StylePittarello, Marco, Massimiliano Scarpa, Aurora Greta Ruggeri, Laura Gabrielli, and Luigi Schibuola. 2021. "Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages" Applied Sciences 11, no. 12: 5377. https://doi.org/10.3390/app11125377
APA StylePittarello, M., Scarpa, M., Ruggeri, A. G., Gabrielli, L., & Schibuola, L. (2021). Artificial Neural Networks to Optimize Zero Energy Building (ZEB) Projects from the Early Design Stages. Applied Sciences, 11(12), 5377. https://doi.org/10.3390/app11125377