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Thermal insulation plays a crucial role in reducing building energy consumption and improving economic efficiency. In this study, a comprehensive thermo-economic model was developed to determine the optimum insulation thickness of building walls by considering both heating and cooling energy demands. Unlike conventional insulation analyses that focus only on heating requirements, the proposed model incorporates both heating degree days (HDD) and cooling degree days (CDD), enabling a full-year evaluation of building energy performance. The life-cycle cost method was used to determine the economically optimal insulation thickness, while numerical optimization was applied to minimize the total cost function. The results indicate that the optimum insulation thickness for expanded polystyrene under the investigated climatic conditions (HDD=2022°C⋅days,CDD=650°C⋅days) is approximately 0.07–0.09 m. Applying this insulation level can reduce the annual energy cost by about 35–50% compared with an uninsulated wall. The economic analysis also shows that the insulation investment can be recovered within 3–7 years, depending on climatic conditions. Furthermore, the results demonstrate that both HDD and CDD significantly influence insulation design, highlighting the importance of full-year climate considerations. The developed model provides a practical tool for determining economically optimal insulation strategies in different climatic regions.
A comprehensive thermo-economic model was developed to determine the optimum insulation thickness of building walls by considering both heating and cooling energy demands. The analysis was based on the heating degree day (HDD) and cooling degree day (CDD) approach, combined with life-cycle cost evaluation. Heat transfer through the wall was assumed to be one-dimensional and steady-state, while the thermal properties of insulation materials, indoor comfort temperature, and system efficiencies were taken as constant. Expanded polystyrene and rock wool were considered as insulation materials. The model included wall thermal resistance, insulation thermal conductivity, fuel and electricity prices, heating system efficiency, cooling system COP, economic lifetime, interest rate, and inflation rate. A numerical optimization procedure was implemented in Python using NumPy and Matplotlib, where insulation thickness was varied within the range of 0–0.20 m to identify the minimum total cost.
The results showed that increasing insulation thickness significantly reduced annual heating and cooling energy costs, while insulation investment increased linearly with thickness. For the studied climatic conditions, the optimum insulation thickness for expanded polystyrene was found to be approximately 0.07–0.09 m, whereas slightly lower values were obtained for rock wool due to its higher thermal conductivity and material cost. The annual energy cost was reduced by about 35–50% compared with an uninsulated wall, and the payback period was estimated to be in the range of 3–7 years. In addition, the optimum insulation thickness increased with increasing present worth factor, heating degree days, and cooling degree days, confirming that both climatic and economic parameters strongly affect insulation design and long-term
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