Optimisation on the thermal insulation layer thickness in buildings with environmental analysis: an updated comprehensive study for Turkey’s all provinces

Abstract

This study determines the optimum insulation layer thickness to be applied to external building walls considering the heating degree-day (HDD) method, then energy saving costs, payback periods, and carbon dioxide (CO 2 ) emissions are calculated accordingly. The optimisation analysis is performed for four different thermal insulation materials (glass wool, rock wool, extruded polystyrene, and expanded polystyrene). Natural gas is chosen as fuel for heating purposes, and horizontal perforated brick is preferred in the wall. One of the original features in this study is environmental analysis to determine the CO 2 emission for the insulated wall in Turkey provinces. Another feature is that it has the most up-to-date data about HDD values and fuel and insulation material costs. The worst and best insulation materials are obtained as rock wool and glass wool, respectively. The optimum insulation layer thickness for the best case is varied between 0.07 m and 0.23 m, depending on the HDD values of provinces. The annual total energy saving cost is in the range of 4.4-53.5 $/(m 2 year), and the payback period is 0.11-0.38 years. Besides, the reduction in annual CO 2 emission is changed between 53.2% and 94% for the best case, compared to the uninsulated wall.

A Tool for Calculating the Building Insulation Thickness for Lowest CO2 Emissions—A Greenlandic Example

“…E heat is an expression for the emissions related to the heat loss of the insulation or wall, depending on the approach. It is calculated by Equation (6), where E factor is the emission factor, quantifying the amount of kg CO 2-eq per kWh heat:…”

“…This approach has been used in other studies. For example, the three Turkish papers [5][6][7] all used a Life Cycle Assessment (LCA) method to analyze a fictive brick-wall case. They all reached different conclusions on the optimal insulation thicknesses for the individual cases, which makes it difficult to compare the results.…”

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

“…The important factors in the field of reducing energy consumption in buildings are climate conditions and insulation thickness. The optimal thickness of the advanced seals and insulations based on the climate conditions of the cities are considered as a great concern for many researchers (Ertürk and Keçebaş, 2021;Kon et al, 2021;Alfarawi et al, 2022;;Aktemur et al, 2021). The results of the studies proved that energy saving through the insulations method can create effective influences on the constructions industries (Gustavsson and Piccardo, 2022;Aydin and Biyikoğlu, 2021).…”

Sicak İkli̇m Şartlari Altinda İç Di̇zayn Sicakliği, Termal Özelli̇kler Ve Yalitim Kalinliğinin Beli̇rlenmesi̇

“…To estimate the heat transition loads used in the determination of optimization of insulation thickness, many works in the literature applied degree-day or degree-hour method which is a crude and simple model under static conditions (Akan, 2021;Aktemur et al, 2021;Bolattürk, 2008;Dombaycı et al, 2006;Hasan, 1999;Çomaklı and Yüksel, 2003;Sisman et al, 2007;Ozkahraman and Bolattürk, 2006;Çomaklı and Yüksel, 2004;Dombaycı, 2007;Dombaycı et al, 2017;Ertürk, 2016;Ertürk, 2017;Yıldız et al, 2008;Kaynaklı, 2008;Mahlia and Iqbal, 2010;Kurt, 2010;Bolattürk, 2006;Kaynaklı et al, 2015;Yu et al, 2009;Özel et al, 2015;Barrau, 2014;Sundarama and Bhaskaran, 2014). However, a more accurate estimation of heating and cooling transition loads is quite important in the determination of the optimum insulation thickness.…”