# Inventive Design for Decision-Making: Cutting Costs and Reducing Carbon Footprint in Buildings
## Refurbishment

Hicham Chibane, Ulrich Bogenstütter, Rémy Houssin, Amadou Coulibaly,
Rabih Slim, Stylianos Chalkidis

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Hicham Chibane, Ulrich Bogenstütter, Rémy Houssin, Amadou Coulibaly, Rabih Slim, et al.. Inventive Design for Decision-Making: Cutting Costs and Reducing Carbon Footprint in Buildings Refurbishment. 24th IFIP WG 5.4 International TRIZ Future Conference, TFC 2024, Nov 2024, Cluj-Napoca, Romania. pp.309-320, 10.1007/978-3-031-75923-9_20. hal-04813496

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# Inventive Design for Decision-Making: Cutting Costs and Reducing Carbon Footprint in Buildings Refurbishment

Hicham Chibane<fnref n="1" />(✉), Ulrich Bogenstütter<fnref n="2" />, Rémy Houssin<fnref n="1" />, Amadou Coulibaly<fnref n="1" />, Rabih Slim<fnref n="2" />, and Stylianos Chalkidis<fnref n="2" />

**Abstract.** With the planet facing escalating degradation due to mass production and increasing carbon emissions and waste, the buildings sector is at the core of innovation to build or optimize existing buildings, particularly in terms of adopting the most appropriate technologies, considering user behavior, and reducing carbon emissions. This research emphasizes the significance of a comprehensive and inventive approach in addressing the challenges of contemporary building sector practices. Using the Inventive De-sign Methodology (IDM), we propose a decision-support method for an evaluation tool for simulating and calculating the life-cycle costs, energy efficiency, and environmental impact of buildings. This tool is named NUKOSI RE. TRIZ is combined with the Design of Experiments Analysis to identify the most critical parameters of NUKOSI RE and avoid compromises between them. By better understanding the relationships (contradictions) among various parameters and recognizing the limitations of current models, we can help decision-makers pave the way for more effective and sustainable construction measures in the future.

**Keywords:** Inventive design · Buildings construction · cost · energy consumption · Carbon footprint

## 1 Introduction

Inventive design is often used to solve complex problems where traditional design is limited to optimization compromises. In this work, the study proposes using inventive design to address issues related to reducing the carbon impact of building construction. According to the European Commission, the construction sector, accounting for over 40% of global energy consumption, is one of the primary contributors to CO₂ emissions [1]. This observation places this sector at the forefront of efforts to construct or improve existing buildings, particularly by reducing carbon emissions and adopting technologies best suited to user behavior. Building performance is crucial as CO₂ emissions are closely linked to the energy mix used in buildings within a given country. It depends on various

<footnote n="1">¹ CSIP/ICUBE - INSA Strasbourg, 24 bd de la Victoire, 67084 Strasbourg, France
Hicham.chibane@insa-strasbourg.fr</footnote>

<footnote n="2">² Mainz University of Applied Sciences, Holzstraße 36, 55116 Mainz, Germany</footnote>
factors such as mate-rials used, efficiency of heating and insulation systems, climatic conditions, and user behaviors. Therefore, our scientific challenge lies in the necessity to develop a model, method, or tool to assist decision-makers in estimating, evaluating, and addressing a multitude of these factors early and quickly, which may sometimes be conflicting. This requires combining inventive design, life cycle cost analysis (LCC), and life cycle assessment (LCA) to propose innovative, sustainable, and effective solutions. In the next section, to highlight this study, we start with a state of the art regarding some of the famous tools of LCA, LCC, and carbon footprint assessment in the real estate sector to lead to a brief overview of NUKOSI RE, the tool to be improved by Inventive design methodology (IDM). Then, we follow up with the progress of the latest research studies undertaken by the CSIP team on the IDM research work, leading to the use of Design of Experiments (DoE) analysis to develop contradictions in IDM. In the fourth part, we outline the overall objective of this study, namely the application of inventive design to optimize workflow and performance of NUKOSI Re footprint evaluation. Then, we conclude, pointing out the limitations and our future perspectives of this study.

# 2 Introduction Buildings Carbon Footprint Evaluation

## 2.1 A Subsection Sample LCC, LCA and Carbon Footprint Evaluation in Building Stocks

Many recent private and public sector studies have sought to isolate or holistically assess energy consumption, life cycle assessment (LCA), and life cycle cost (LCC) in the real estate sector thematically in isolation or holistically, which, as mentioned above, is relevant to environmental impacts. This has led to the development of methods and software tools for carrying out these assessments. To assess the sustain-able quality of buildings internationally, various certification systems for buildings have been developed that integrate benchmarks of LCA and LCC from the construction, use and end-of-life phases of buildings of different use categories (e.g., BNB, BREAM, CASBEE, DGNB, Greenstar, HQE, ITACA, LEED, NahWoh, NF). These are generally new construction projects. Certifications are generally voluntary. Loga et al. 2010 created a methodological framework for segmenting and classifying building stocks following building typologies [2] In 7 tasks by Tabula tool, an Excel workbook, user can assess the energy performance of buildings by 1) defining the building types for your country, then 2) choosing examples of buildings, pictures, and data, 3) identifying the supply systems for heating and hot water. This allows to calculate the energy performance enabling to image the national building stock [3]. With the CAALA analysis software tool, based on CAD and BIM integration, a plug-in for SketchUp and Rhino models, users can balance, optimize, and communicate their building projects [4]. Via the Bombyx tool, based on SQL material and component database, users can select from various types of materials for building systems, enabling them to modify smartly the building's geometry for a real-time assessment of the environmental impact [5]. To consider the economic and ecological of a sustainable retrofit decision for the decarbonization of the real estate sector, Hirsch, et al. (2019) developed a tool Carbon Risk Real Estate Monitor (CRREM) for monitoring carbon emissions [6]. It enables GHG reduction pathways by simulating different virtual retrofits to assess their impact on corporate energy and carbon performance.
There are many other tools and software solutions on the market for balancing primary energy demand, non-renewable and greenhouse gas emissions in the building life cycle. Most are significant based on statistical analysis [7]. The use of LCA tools is largely voluntary. But they are either complicated, and the user needs time and the right expertise to perform the tasks, or they are not holistic and do not cover all the required functions to get efficient and detailed analysis results.

## 2.2 NUKOSI RE

After a cooperation project starting in 2016 between, the University of Applied Sciences Mainz and the LBB (State Office for Real Estate and Construction), a software, called NUKOSI RE, was developed from 2023 by Ulrich Bogenstätter. The target was to develop an easy-to-use solution for investors to calculate LCA and LCC, which quickly achieves valid results and allows a variety of variants to be automated. NUKOSI RE is based on an official environmental standard, LCC, and LCA. After inputting project information, NUKOSI RE predicts the situation of a building, providing scenarios for assessment (modernization, repair with measures, new built, etc.). This scenario provides an ideal solution that helps the property manager to make the optimum decision in both the short and long term. Bogenstätter and Slim offer a detailed explanation of the tool. It allows us to calculate scenarios, operations, and repairs during the expected lifetime of a building [8]. NUKOSI RE has some limitations and offers possibilities to become smarter concerning their parameters. Inventive Design Methodology (IDM) is crucial to these improvements, which is the topic of the next section, and the overall objective of this study.

# 3 Design of Experiments (DoE) and Inventive Design Methodology (IDM)

Experimental modeling first emerged between 1920 and 1930 thanks to the research of the pioneer of statistics, Ronald A. Fisher. He demonstrated that flaws in the design of experiments generating data often hampered system data analysis. Fisher's work was later analyzed and developed by George Edward Pelham Box in 1978. An important phase of experimental modeling began with the introduction of the Response Surface Methodology (RSM) into the industrial world by Box and Wilson. RSM and other experimental modeling techniques have since become widely adopted in industry, primarily due to Box's research and more recently, the work of Box and Draper [9] and Box et al. [10]. Design of Experiments (DoE) is a powerful data analysis tool that enables the understanding and study of various complex processes. The use of DoE also allows for a reduction in the number of experiments necessary for process understanding. The work of Genichi Taguchi (1986) had a significant impact on the development of DoEs. Taguchi proposed highly fractionated factorial designs and other orthogonal arrays, along with new statistical methods to solve various problems.

DoEs are an effective tool for analyzing the influence of process variables on specific properties. Appropriate data can be analyzed using statistical methods such as RSM and
multiple linear regression analysis. Experimental data can be fitted using a second-order polynomial response surface model, as expressed in following Equation.

$$Y = \beta_0 + \sum_{i=1}^{N} \beta_i X_i + \sum_{i=1}^{N} \beta_{ii} X_i^2 + \sum_{i=j}^{N} \beta_{ij} X_i X_j + \omega$$

where Y is the predicted response, the parameter β₀ is the model constant, βᵢ is the linear coefficient, βᵢᵢ is the quadratic coefficient and βᵢⱼ is the cross-product coefficient. Xi and Xj (i < j) are the independent variables that are known for each experimental run. ω is the experimental error term.

To make the Methodology of Industrial Problem Solving and Design more effective, researchers have combined the power of DoE for data analysis with new inventive design methods based on the understanding provided by these designs. Chibane et al. integrate optimization into this methodology by using DoE to formulate contradictions and the Pareto chart to select the optimal solution when no single solution exists that simultaneously optimizes each objective [11]. Based on Design of Experiments Analysis, Dubois et al. provide two methods to help choose the priority problem by weighting the relationships between the parameters to determine the effect of several inputs (action parameters) on desired outputs (evaluation parameters) [12].

The IDM method has been applied recently to various case studies at universities and companies covering different sectors (machinery and automotive equipment, manufacturing industry, information technology, etc.) and for various complex systems (product, system, software). A few applications concern the construction sector, the building assessment following LCA and LCC, and the Carbon footprint, which we highlight in the next part following the Design of Experiments analysis in IDM.

# 4 Application for Evaluation of Cutting Costs and Buildings Carbon Footprint

## 4.1 Design of Experiments Analysis in IDM

NUKOSI RE, presented above, simulates the performance of a construction based on various construction-related parameters. We use advancements in inventive design to solve problems related to building renovation (refurbishment) and construction and its impact on ecology. The proposed method consists of the following steps, as shown in Fig. 1.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Steps of applying IDM in NUKOSI RE parameters.</figcaption></figure>
## 4.2 Case Study: NUKOSI RE Parameters

### Step 1: Initial problem analysis:

In this step, all the parameters affecting and evaluating the system are listed, along with information about their type (qualitative or quantitative). Additionally, the levels of action parameters and their importance are determined.

For illustrative reasons, we selected only five action parameters and five performance evaluation parameters of the construction, as shown in Table 1.

<table><caption>**Table 1.** Classifications of NUKOSI RE parameters.</caption><thead><tr><th>Action parameters</th><th>Level_1</th><th>Level_2</th><th>Level_3</th></tr></thead><tbody><tr><td>AP1_Air conditioning (factor)</td><td>0</td><td>10</td><td>100</td></tr><tr><td>AP2_Building Age (class)</td><td>1</td><td>2</td><td>3</td></tr><tr><td>AP3_Garage</td><td>0</td><td>22.5</td><td></td></tr><tr><td>AP4_Modernization (type)</td><td>Modernization (moder)</td><td>Operation (Oper)</td><td>New build</td></tr><tr><td>AP5_Energy source (type)</td><td>Gaz</td><td>Photovoltaic (Phot)</td><td>Wood</td></tr><tr><td colspan="4">Evaluation parameters</td></tr><tr><td colspan="4">EP1_Building costs (euro)</td></tr><tr><td colspan="4">EP2_Energy cost (euro)</td></tr><tr><td colspan="4">EP3_Maintenance cost / Invest (euro)</td></tr><tr><td colspan="4">EP4_Energy balance (KWh/ GFA) (GFA Gross floor area)</td></tr><tr><td colspan="4">EP5_CO<sub>2</sub> equivalent (CO<sub>2</sub>e/GFA)</td></tr></tbody></table>

The action parameters were essentially selected according to the fact that they do not directly influence each other. Modernization (energy modernization, no investment in the existing building, new construction) describes the energy standard achieved by the building materials (e.g., exterior walls, windows, roof). This also includes the efficiency of the installed heating and ventilation system. The energy standard influences the energy consumption, the life cycle assessment, and the repair costs. Air conditioning (0%, 10%, 100%) describes the share of the mechanical ventilation system in the air exchange. The user often wants window ventilation. However, a mechanical ventilation system enables a heat recovery system, with window ventilation, the heated air escapes unused. Air conditioning therefore has a particular influence on energy consumption and operating costs. The selected building age classes (1 (1965), 2 (1990), 3 (2020)) allow conclusions to be drawn about the (then) valid energy standard and the building materials used. The energy standard influences the energy consumption and operating costs, the building materials for the life cycle assessment, and the maintenance costs. The type of
energy source (gas, photovoltaic to 100% green electricity, wood) is decisive for the life cycle assessment. The proportion of the garage influences the energy consumption and operating costs, the building materials for the life cycle assessment, and the repair costs.

The evaluation parameters are differentiated according to LCC and LCA. LCC evaluation parameters assess 1. The investment costs required today for new construction or modernization, 2. The energy costs and 3. The repair costs during a 50-year building use. LCA evaluation assesses the energy consumption in [KWh/m2 (GFA)] and the CO2 emissions in [CO2 (eq.)/m2 (GFA)].

## Step 2: Design of experiments:

The second step involves preparing a plan of experiments, which defines the combinations of action parameters to be tested.

Conducting tests: This part is carried out using the NUKOSI RE software. Different combinations of action parameters are tested, and the construction's performance is simulated based on these scenarios.

The chosen design of the experiment (DoE) is a fully factorial design with five fac-tors, each having 3 or 2 levels, resulting in a total of 162 experiments.

As this is a numerical design, each experiment was conducted without replication, as shown in Table 2.

<table><caption>**Table 2.** Experimental modeling.</caption><thead><tr><th>Test</th><th>AP1</th><th>AP2</th><th>AP3</th><th>AP4</th><th>AP5</th><th>EP1</th><th>EP2</th><th>EP3</th><th>EP4</th><th>EP5</th></tr></thead><tbody><tr><td>1</td><td>100</td><td>1</td><td>0</td><td>New build</td><td>Gaz</td><td>17288293</td><td>17268154</td><td>13179467</td><td>160536360</td><td>41954067</td></tr><tr><td>2</td><td>0</td><td>2</td><td>22,5</td><td>oper</td><td>Phot</td><td>0</td><td>17097123</td><td>13020724</td><td>45762167</td><td>3479301</td></tr><tr><td>3</td><td>10</td><td>3</td><td>22,5</td><td>moder</td><td>Wood</td><td>8510208</td><td>3593731</td><td>10521320</td><td>24557335</td><td>4261782</td></tr><tr><td>4</td><td>0</td><td>1</td><td>0</td><td>New build</td><td>Wood</td><td>17288293</td><td>8801433</td><td>13179467</td><td>87667563</td><td>7140458</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>160</td><td>0</td><td>2</td><td>22,5</td><td>New build</td><td>Gaz</td><td>18922866</td><td>5952697</td><td>13020735</td><td>45762167</td><td>13919541</td></tr><tr><td>161</td><td>100</td><td>3</td><td>0</td><td>oper</td><td>Gaz</td><td>0</td><td>4523853</td><td>10669281</td><td>27309693</td><td>8563729</td></tr><tr><td>162</td><td>100</td><td>1</td><td>0</td><td>moder</td><td>Wood</td><td>24660045</td><td>14657348</td><td>13179480</td><td>160536360</td><td>8970287</td></tr></tbody></table>

After conducting the tests, experimental modeling is performed using the method of experiments and linear regression. This helps understanding how different action parameters influence the construction's performance (evaluation parameters) and its ecological impact. The first modeling conducted is that of investment cost based on different action parameters; the following Table 3 shows the mathematical models representing this relationship. The correlation coefficient of this model is 95.78%.

The Pareto diagram, as shown Fig. 2 determine the value and importance of factor effects. On this diagram, the bars that intersect the reference line are statistically significant.

Main Effects Plot indicates that the most influential parameter on EP1 is AP2, followed by AP3 (negatively) and AP4 (positively). Parameters AP1 and AP5 do not have
<table><caption>**Table 3.** Experimental modeling.</caption><tr><td>Moder</td><td>EP1</td><td>= 38759857 - 10565692*AP2 + 555673*AP3 - 86306*AP2*AP3</td></tr><tr><td>New build</td><td>EP1</td><td>= 15346401 + 970946 * AP2 + 245260 AP3 - 86306*AP2*AP3</td></tr><tr><td>Oper</td><td>EP1</td><td>= -1941892 + 970946*AP2 + 172613* AP3 - 86306*AP2*AP3</td></tr></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Pareto diagram.</figcaption></figure>

a significant influence on EP1. The validation of this interpretation of the graph is conditioned by statistical significance by observing the effects in the previous analysis of the variance table. In this case, the influential effects are indeed significant (Fig. 3).

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Assessment of AP.</figcaption></figure>

Similarly, EP2, EP3, EP4, and EP5 were modeled and analyzed.

### Step 3: Experimental modeling- Multi Objective optimization:

After conducting the tests, experimental modeling is performed using the method of experiments and linear regression. This helps us to understand how different action parameters influence the construction's performance and its ecological impact. A study on the effects of interactions between action parameters was conducted. We chose to focus on showing the results of main effects. At the end of this modeling process, a network of influential parameters was extracted. This allowed us to understand the
influences of the parameters, correct certain functions in the NUKOSI RE software, and update the DoE. The models extracted in this analysis were inputted into a multi-objective optimization algorithm. The results of this modeling and the multi-objective optimization of solutions and parameters are shown in Tables 4 and 5 respectively.

<table><caption>**Table 4.** Results of modeling Multi objective optimization of evaluation parameters.</caption><thead><tr><td>Sol</td><td>Objective</td><td>Target</td><td>Superior</td></tr></thead><tbody><tr><td>EP5_CO2_Emis</td><td>Minimum</td><td>2640817</td><td>41954067</td></tr><tr><td>EP4_Energy_Balance</td><td>Minimum</td><td>24034503</td><td>160536360</td></tr><tr><td>EP3_Maintenance</td><td>Minimum</td><td>10521320</td><td>15120477</td></tr><tr><td>EP2_Energy_cost</td><td>Minimum</td><td>3569543</td><td>59977708</td></tr><tr><td>EP1_Cost_Invest</td><td>Minimum</td><td>0</td><td>39120361</td></tr></tbody></table>

<table><caption>**Table 5.** Results of modeling Multi objective optimization of solutions (APs values).</caption><tr><td>AP1</td><td>AP2</td><td>AP3</td><td>AP4</td><td>AP5</td></tr><tr><td>Air_cond</td><td>Const_ge</td><td>Garage</td><td>Const_type</td><td>Energie</td></tr><tr><td>100</td><td>3</td><td>22,5</td><td>operation</td><td>Holz</td></tr><tr><td>EP5_</td><td>EP4_</td><td>EP3_</td><td>EP2_</td></tr><tr><td>CO2_Emis</td><td>Energy_Balance</td><td>Maintenance</td><td>Energy_cost</td></tr><tr><td>2938461</td><td>26786712</td><td>10588187</td><td>1692340</td></tr></table>

### Step 4: Extraction of contradictions:

At this step, contradictions in the system are identified. Contradictions may arise when certain configurations of action parameters improve one aspect of construction performance but negatively affect another aspect. Identifying these contradictions is crucial for improving the design. We strive to minimize all action parameters. Therefore, we have chosen to set the target for each action parameter at the minimum value of the experimental plan.

The first step involves preparing the experimental plan for contradiction extraction. To achieve this, we begin by binarizing the experimental plan based on a given threshold. Binarization entails transforming the values of evaluation parameters into binary digits, either 0 or 1 (or into color codes red and green). The code 0 is assigned when the evaluation parameter does not meet the desired objective (red cell), while the code 1 is assigned when the parameter meets the objective (green cell). It is important to note that the binarization threshold is defined beforehand based on the required criteria. This threshold ranges from 0 to 100%; 0% corresponds to the ideal value for the evaluation parameter, meaning the value closest to the desired objective in the experimental plan, while 100% represents the most unfavorable value.

Subsequently, the contradiction extraction step involves checking, for each pair of two levels of an action parameter (with other parameters held constant), whether the set
of experimental points (EPs) satisfies at least one line of the two selected levels of EPs. This process, often complex, requires numerous iterations. For this reason, we have developed an application as part of the VirtFac project to automate the contradiction extraction process using a software tool. This application is utilized for this work.

The identification of contradictions concluded that a contra-diction appearing with a single action parameter is observed with the parameter AP5, as shown in Fig. 4.

<figure><img src="image_7.png" /><figcaption>Fig. 4. System of contradiction.</figcaption></figure>

The Table 6 summarizes all the contradictions. It shows that the contradiction presents on the parameter AP5 (Energy).

<table><caption>Table 6. Summary of Contradictions.</caption><thead><tr><th rowspan="2">Id</th><th colspan="5">Action parameters</th><th colspan="5">Evaluation parameters</th></tr><tr><th>AP1</th><th>AP2</th><th>AP3</th><th>AP4</th><th>AP5</th><th>EP1</th><th>EP2</th><th>EP3</th><th>EP4</th><th>EP5</th></tr></thead><tbody><tr><td>7</td><td>10</td><td>3</td><td>22,5</td><td>oper</td><td>Phot</td><td>0</td><td>9174823</td><td>10521320</td><td>24557335</td><td>2949334</td></tr><tr><td>10</td><td>10</td><td>3</td><td>22,5</td><td>oper</td><td>Gaz</td><td>0</td><td>3915245</td><td>10521320</td><td>24557335</td><td>8323654</td></tr><tr><td>7</td><td>10</td><td>3</td><td>22,5</td><td>oper</td><td>Phot</td><td>0</td><td>9174823</td><td>10521320</td><td>24557335</td><td>2949334</td></tr><tr><td>118</td><td>10</td><td>3</td><td>22,5</td><td>oper</td><td>Wood</td><td>0</td><td>3593731</td><td>10521329</td><td>24557335</td><td>4261782</td></tr><tr><td>21</td><td>100</td><td>3</td><td>22,5</td><td>oper</td><td>Wood</td><td>0</td><td>3811424</td><td>10521321</td><td>24034503</td><td>4360180</td></tr><tr><td>47</td><td>100</td><td>3</td><td>22,5</td><td>oper</td><td>Phot</td><td>0</td><td>8979489</td><td>10521323</td><td>24034503</td><td>2949334</td></tr><tr><td>24</td><td>0</td><td>3</td><td>22,5</td><td>oper</td><td>Gaz</td><td>0</td><td>3893701</td><td>10521321</td><td>24615428</td><td>8346121</td></tr><tr><td>145</td><td>0</td><td>3</td><td>22,5</td><td>oper</td><td>Phot</td><td>0</td><td>9196527</td><td>10521331</td><td>24615428</td><td>2949334</td></tr><tr><td>47</td><td>100</td><td>3</td><td>22,5</td><td>oper</td><td>Phot</td><td>0</td><td>8979489</td><td>10521323</td><td>24034503</td><td>2949334</td></tr><tr><td>150</td><td>100</td><td>3</td><td>22,5</td><td>oper</td><td>Gaz</td><td>0</td><td>4109144</td><td>10521332</td><td>24034503</td><td>8121455</td></tr><tr><td>89</td><td>0</td><td>3</td><td>22,5</td><td>oper</td><td>Wood</td><td>0</td><td>3569543</td><td>10521327</td><td>24615428</td><td>4250849</td></tr><tr><td>145</td><td>0</td><td>3</td><td>22,5</td><td>oper</td><td>Phot</td><td>0</td><td>9196527</td><td>10521331</td><td>24615428</td><td>2949334</td></tr></tbody></table>
The Table 7 shows the most favorable configurations. The solution emerging from this inventive analysis involves combining the use of two types of energy to meet all the criteria for evaluating constructions. Alternatively, another energy source could be proposed. This analysis is only preliminary, offering an initial focus on the weighting of performance evaluation parameters and the operational principles of each parameter. To better guide the study, a more comprehensive analysis over time and space is necessary. This will provide a deeper understanding of the dynamics and interactions of the various elements involved. Resolving these contradictions does not bring any benefit since we encounter contradictions with only one action parameter for the same threshold (1%).

<table><caption>**Table 7.** The most favorable configurations.</caption><thead><tr><td>AP1</td><td>AP2</td><td>AP3</td><td>AP4</td><td>AP5</td></tr></thead><tbody><tr><td>0-10-100</td><td>3</td><td>22.5</td><td>Oper</td><td>Wood/Gaz or Phot</td></tr></tbody></table>

**Step 5: Innovation and proposing model changes:**

Finally, after identifying the contradictions, the decision-maker can propose innovations and model modifications to address construction-related issues and their environmental impact. This may involve adjusting action parameters, utilizing more environmentally friendly materials, or exploring other creative solutions to enhance the overall construction design. This iterative process of simulation, analysis, and proposal of changes facilitates the exploration of various solutions and the development of more ecologically sustainable construction approaches. Therefore, new solutions and technologies must be continuously integrated into the simulator. For instance, in our case, the contradiction to resolve involves the fact that the heating system needs to be photovoltaic to meet the performance requirements PE5 and wood-based to meet the PE 1,2,3 and 4. We can thus envision a new design for the heating system that combines both technologies. By applying the TRIZ inventive principle of separation, we propose a temporal separation approach, utilizing each heating system at different times and incorporating an automated system that monitors air quality in real-time. As illustrated in Fig. 5, the emerging hybrid boiler/photovoltaic technology shows promise in addressing specific issues. To further advance this study, this type of heating should be integrated into the NUKOSI RE simulator to assess the relevance of the proposed solutions.
<figure><img src="image_8.png" /><figcaption>Fig. 5. Hybrid Wood / Photovoltaic heating solution.</figcaption></figure>

**5 Conclusion**

This research highlights the importance of a comprehensive and inventive approach to addressing current challenges in the building sector. We have developed a decision-support method for an evaluation tool that simulates life-cycle costs, energy efficiency, and environmental impact of buildings using the Inventive Design Methodology (IDM). By combining TRIZ with Design of Experiments analysis, we identify critical parameters and avoid compromises between them. Understanding the contradictions and limitations of current models helps guide decision-makers toward more effective and sustainable construction measures. The results demonstrate that the continuous integration of new solutions and technologies, such as hybrid boiler/photovoltaic systems, is essential for advancing towards more ecological and sustainable buildings. This iterative process of simulation, analysis, and proposing changes paves the way for promising advancements in the building sector's future.

The aim of this study is primarily methodological, focusing on the development of new methods for solving complex problems based on inventive design. It is also applied to a contemporary issue: reducing building pollution. The proposed solution, although seemingly basic, effectively addresses the initial problem. However, a limitation of the method is the duration of data processing. Indeed, further in-depth studies, including additional calculations and verifications, are required to refine this solution concept, which could enhance the efficiency and accuracy of the approach.
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