# Development of ZEB Design Support Tool for Non-Residential Buildings

Shoichiro TOYODA

Suzu TOYODA

##### Theme

We tackled the issue of environmental concerns related to the construction industry. It has been reported that approximately 40% of the world's CO2 emissions originate from construction-related activities. In response, there is a global push to increase the construction of Net Zero Energy Buildings (ZEB), which aim for a net zero energy consumption balance annually. Achieving ZEB requires significant energy savings. It is crucial to appropriately combine advanced equipment and materials within certain constraints to minimize the energy load of buildings. In the 2023 MITOU Target Program, we aimed to contribute to the spread of environmentally friendly buildings by developing 'ZEBOpt,' an energy-saving architectural design support tool that utilizes annealing machines to reverse-calculate exterior designs from energy-saving building standards of IBECs.

##### Details of the combinatorial optimization problems

The exterior design consists of walls, windows, roofs, etc. Referencing prior research, we translated the design patterns of the exterior into combinations of types of building materials and the area in which they are used. This sets up an optimization problem to explore combinations of building materials that are both meeting the required thermal performance and reducing building material costs.

###### Decision variables

Two-dimensional decision variables, types of building materials (wall, roof, windows, etc) (i) and the area (j)

Image of decision variables

Area (m2) (j)
5 10 15 ・・・
Materials (i) Wall A q (0 or 1) q (0 or 1) q (0 or 1) q (0 or 1)
Wall B q (0 or 1) q (0 or 1) q (0 or 1) q (0 or 1)
Window C q (0 or 1) q (0 or 1) q (0 or 1) q (0 or 1)
Roof D q (0 or 1) q (0 or 1) q (0 or 1) q (0 or 1)

###### Objective function
• Minimize the Building Performance Index (BPI), which is an indicator of a building's insulation performance (the lower, the better for insulation)
• Minimize the total cost of building materials for the envelope
• Color match between exterior wall/roof material and window material
• Compatibility of building materials
###### Constraints
• Exterior wall area of each orientation (Orientation: East, West, North, South, Roof)
• Window area for each orientation
##### Challenges

When I was pursuing my master's, I conducted research using GA on a similar theme, referencing international research. This time, since the tool is targeted at Japan, we are using Japanese standards for the Building Palstar Index (BPI), which indicates the insulation performance of buildings. However, it was difficult to find any literature that clearly defines the Japanese BPI standards in mathematical terms, and initially, it was challenging to formulate it into an equation.

Additionally, while there is plenty of design-related information available on building materials, there is a lack of data on physical properties, and no comprehensive database exists. Therefore, for this project, we used mock data while referring to the materials for which physical property values were available.

##### Future Outlook

Selecting building materials that balance insulation performance with cost is crucial, and considering future regulations, we believe there is significant demand in this field. Currently, there is a shortage of user-friendly tools, so we're considering releasing this as a service in the future. As future developments, we plan to improve input data and preprocessing, tweak decision variables, and enhance the user interface. In terms of business, we believe it is essential to build a system or platform that allows many building material manufacturers and designers to participate. We're also interested in developing this collaborative framework.

##### Achievements / Gratification

Initially, we considered handling the BPI with inequality constraints, such as specifying it to be below a certain value. However, since the calculation of BPI involves absolute values, we decided to square it and treat it as an objective function (soft constraint) to make it manageable with an annealing machine. As a result, we were pleased to be able to offer unique value by suggesting various combinations of building materials that are close to the target BPI value.

##### To Viewers

In Japan, where industries requiring complex manufacturing processes thrive, there are many combinatorial optimization problems to be solved. However, it's often challenging for software engineers alone to even identify these problems. I believe there is great value in having professionals from various fields work on these optimization challenges. With the availability of tools like Fixstars Amplify, which make it easier for those who are not experts in mathematical optimization to tackle optimization problems, I am hopeful that more people can engage in combinatorial optimization without being intimidated by their level of experience. we would be delighted if we could work together to boost the manufacturing industry.