1. ASHRAE and IBPSA-USA SimBuild 2016: Building
Performance Modeling Conference
Steps Toward Designing a Positive
Energy House: Lessons Learnt
Amir Rezaei Bazkiaei, PhD, BPAC, LEED GA
MKK Consulting Engineers Inc.
Innovation Lab
arezaei@mkkeng.com
Ph: (303) 796-6037
Raghuram Sunnam,
Baumann Consulting,
r.sunnam@baumann-us.com
Ph: (202) 608-1334
2. Outline/Agenda
• Big Data and AEC Industry
• Case Study 1 – Positive Energy House in France
• Case Study 2 – Peak Radiant Heating/Cooling Load
• Case Study 3 – Form/Shape Optimization
• Case Study 4 – Off Grid Building Design
• Conclusion
3. Big Data and other Industries
• Cancer research (human genome sequencing)
• Targeted advertising
• Precision agriculture
• Financial analysis
• Politics and news
4. Big Data for AEC Industry
• Rule-of-thumb versus Data-driven Decisions
• Feedback with the Speed of Design
• Targeted and Integrated Approach
• Workflow Optimization
• If you don’t have big data
GENERATE it!
5. Learning Objectives
• Understand the role of optimization and
data visualization techniques to inform
high performance designs
• Distinguish the hurdles in effectively using
energy modeling tools to achieve a high
performance design.
6. Case Study 1
Objectives of studying Net zero energy buildings (EISA,
2007; EPBD, 2010):
- Reduce energy consumption
- Reduce greenhouse gas emissions
- Make operation of buildings more economical
Overall steps adopted to design the net-positive-energy house:
Weather Analytics
Optimize Passive
Strategies
Optimize Mechanical
Systems
Assess On-site
Electricity Generation
Energy Model geometry of
the residence design
7. Climate Analysis
Outside Air Temperature analysis using Climate Consultant 3.0 – Heating and Cooling Demand Analysis
Time period close to Comfort zone:
May – September
8. Climate Analysis
Wind direction analysis using Climate Consultant 3.0 – Cross Ventilation Strategy
Average Wind Speed 2m/s to
4m/s (North-South
Orientation)
10. Envelope Optimization
• Envelope optimization is a typical multiple parameter optimization
problem.
• GenOpt was used to optimize the envelope R-values
• Overall goal of the optimization is to have an optimal cooling and
heating EUI
Parameter Optimization
Range
Increments used
for optimization
Wall R-values R-0.2 to R-17.6 0.1 m²K/W
Roof R-values R-0.2 to R-17.6 0.1 m²K/W
Slab on-grade R-
value
R-0.2 to R-17.6 0.1 m²K/W
Window U-value 0.5 to 3 0.1 W/m²K
Window Solar Heat
gain coefficient
(SHGC)
0.1 to 0.9 0.1
The parameters and the range of values that were optimized
14. Natural Ventilation Optimization
EnergyPlus Fenestration opening controls
• EnergyPlus Airflownetwork model was used for natural ventilation
study of the building
• Key factors of optimization:
• Opening factor of assigned surfaces to Airflownetwork model
• Lower and upper temperature difference (inside vs outside temperature)
• Zone temperature threshold
25. Case Study 4: Off-Grid Energy Design
179.5kW
97.9kW
0.0kW
101.9kW
55.7kW
0.3kW
PV Generation [W]
January February March April May December
Building Electricity Demand [W] - Assembly 7, 62.1 OA levels and no-DCV
January February March April May June
Hours
June July August September October November
Hours
July August September October November December
26. Conclusion
• Large number of variables to optimize
• Knowledge of key competing design variables
• Break optimization into smaller runs
• Choose the right optimization objective
• Right visualization tool for the right metric
• Large data set to explore the solution space
• Flexibility in design decisions
