urban morphology and building pv energy production
TRANSCRIPT
1
Urban morphology and building PV energy production
Ivan, Kin Ho POON
Séminaire SET 2019 – Intelligence artificielle & Energie
7th May 2019
2
Solar Energy Research Institute of Singapore
(Picture source: Eco-
❑ The Solar Energy Research Institute of Singapore (SERIS) is Singapore’s national institute for
applied solar energy research. SERIS’ multi-disciplinary research team includes more than 150
scientists, engineers, technicians and PhD students.
❑ The Urban Solar group works on city-scale 3D mapping tools, optimisation of building typology
and urban morphologies for maximising building solar energy potential.
❑ The group supports various Singapore government initiatives, e.g. “SolarNova” programme and the
“Positive Energy, Zero Energy, and Super Low Energy Buildings for the Tropics” programme
(Picture source: SERIS)
3
(Picture source: Eco-Business)
Singapore:
- Solar energy target: 350MWp by 2020 (current: 150MWp)
4
(Picture source: Wikimedia [top]; NUS [bottom])
1. Urban planning legislation and energy planning is typically separated (IEA SHC 51)
2. Practical use of design-support tools is still limited at the early design stage. (Nault, 2017)
3. Lack of tool and knowledge for the urban planners / government officials to estimate the solar
energy potential of a new planning zone. (Kanters et al., 2015)
4
5
(Picture source: Polis)
5
Urban morphology is proved to be a good indicator to predict building solar energy potential and energy
consumption in district level
Sarralde et al. (2015) found that suitable urban morphology form can increase the solar irradiance by 9% and 45% on roof
and façade in London respectively
Bourdic and Salat (2012) found that the importance of urban morphology in determining the energy consumption of
buildings is almost the same when comparing to other factors
Adapted from: Bourdic and Salat (2012)
ClimateUrban
MorphologyBuilding design
System efficiency
Occupant behaviour
Energy Performance
Adapted from: Bourdic and Salat (2012)
6
Top-down approachBottom-up approach
Background
Agent Based
• Based on the aggregation of individual energy consuming activity
• May be inaccurate due to rough aggregation
• Massive data requirement
• Can assess system improvement but not for large scale improvement programmes
Energy Environment
• Most common nowadays
• Causality between energy consumption and production
• Not able to provide information to improve buildings’ energy efficiency
Economic
• Causality between energy consumption and other variables, e.g. econometric, climate
• Building characteristics are not considered
• Can assess the impact of large scale improvement programmes
Morphological
• Links district energy consumption to urban morphology
• Rare at the moment
• Provide useful insight for urban planning in future
(Loeiz Bourdic & Serge Salat, 2012)
❑ Typical district energy modelling typologies
7
(Picture source: HubSpot Blog)
7
1. Each city is identical – lack of study on finding the relationship between urban
morphology and solar energy potential in Singapore;
2. Urban morphological based energy model is rare, especially for optimization model;
3. Design is not about numbers – some architects and planners are skeptical about the
usefulness of parametric based design supporting tools
4. Machine learning? Non-parametric machine learning has been adopted for other types
of building energy model, but not for morphological based predictive models
Gaps to be filled
8
Overall workflow
1. Simulation
• Data collection for prediction model building
2. Predictive model building
• Parametric study between urban morphology and the performance indicators
3. Optimization model building
• Maximize solar energy potential
• Minimize energy consumption
Objective 1 & 2
• To understand the relationship between urban morphologyand building solar energy potential and energyconsumption in Singapore by parametric study
• To build a morphological based building solar energy andenergy consumption prediction model by machine learningand statistical regression
Objective 3
• To recommend an optimized urbanmorphological form that can maximizesolar energy contribution to buildings’energy consumption of a neighborhoodin Singapore by simulation optimizationand linear programming
9
❑ Urban Morphological Parameters Selection
➢ Solar Energy Potential (9 indicators for façade and 5 indicators for rooftop)
➢ Building Energy Consumption (5 indicators)
Stage 1: Simulation
Façade only
Roof & façade
Type of morphological characteristic Parameters
Vertical & horizontal distribution Average building height
Building geometry Building orientation
Building density
Plot ratio
Site coverage
Height to width ratio
Volume area ratio
Compacity
Degree of randomness Standard deviation of building heights
Others Sky view factor
Type of morphological characteristic Parameters
Vertical & horizontal distribution Average building height
Building density
Plot ratio
Site coverage
Open space ratio
Others Sky view factor
10
Stage 1: Simulation
Scenario Builder
Solar insolation simulation (rooftop)
Solar insolation simulation (façade)
Sky view factor simulation
Energy simulation
Ladybug for Rhino-
Grasshopper
- Adopts RAIANCE as its
simulation engine
Honeybee for Rhino-
Grasshopper
- Adopts EnergyPlus as its
simulation engine
CitySim
- Well validated
- Similar irradiance
simulation engine as
RADIANCE
11
1. Threshold (1200kWh/m2/year) 2. Threshold (1400kWh/m2/year) 3. Threshold (1600kWh/m2/year)
• PV coverage: 95%
• Energy consumption (cooling):
30,791 kWh/year
• PV generation: 10,806 kWh/year
• PV coverage: 77%
• Energy consumption (cooling):
31,051 kWh/year (↑0.84%)
• PV generation: 9,032 kWh/year
(↓16.4%)
• PV coverage: 23%
• Energy consumption (cooling):
31,743 kWh/year (↑3.10%)
• PV generation: 2,906 kWh/year
(↓73.1%)
Stage 1: Simulation
Tallmax framed 72-cell module is selected (Multicrystalline PV cell system; 340W power rating; Size: 1.96 m * 0.99 m)
12
❑ Regression Model
- Multi-regression model (3 models – building energy consumption, solar energy potential on rooftop and façade)
- Significance test (t test with 0.05 significance level )
- Multicollinearity test (Variance inflation factor (VIF))
❑ Other machine learning techniques
- Neural Network (NN)
- Training (70%), validation (15%) and testing (15%)
- K-fold cross validation
- Gaussian process (GP) will also be explored
Stage 2: Predictive Model Building
Objective
• To build a morphological based building solar energy and energy consumption prediction model by machinelearning and statistical regression
13
❑ Simulation optimization
➢ Evolutionary algorithm (EA) is the most frequently type of algorithm used (Nguyen et al. (2014))
➢ Will try to use the hybrid CMA-ES/HDE algorithm in the study (Kämpf and Robinson, 2009, Kämpf et al., 2010,
Kämpf and Robinson, 2010)
➢ Will perform sensitivity analysis around the optimized solution
❑Model-based optimization
➢ Similar to simulation optimization. But a surrogate model is constructed during optimization process
➢ The optimization algorithm can be linked to the predictive model developed by machine learning in order to
reduce computational resource for simulation
Stage 3: Optimization
Objective
• To recommend an optimized urban morphological form that can maximize solar energy contribution to buildings’energy consumption of a neighborhood in Singapore
14
Potential Contribution of the Research
• By MIT
• Demand forecast
• Walkability
• Daylight
• By ETH
• No demand forecast
• System simulation
• Optimization (energy
supply systems
design)
• By EPFL
• Demand forecast
• Solar potential and
electricity production
• Radiant comfort and
UHI effect
• By ETH
• Demand forecast
• Renewable energy
systems potential
• Supply systems
optimization (energy
supply systems
design)
• Morphological based
• Can predict and
suggest an optimized
form
• Suitable to be used
at early urban
planning stage
Source: ETH FCL
❑ Comparison with other district energy models
- Morphological based district building energy model is rare. The causality between morphology and energy
supply/demand is not taken into account; and
- District scale energy optimization model is lacked.
16