Thermal performance and passive energy saving optimization of prefabricated houses in Xinjiang region

Xinjiang uygur autonomous region in China experiences low winter temperatures and strong solar radiation. Actual test results showed that the indoor temperature of a conventional prefabricated house dropped to −10.02 °C at night, resulting in high heating energy consumption of 80.08 kWh. After insulation transformation, the minimum temperature improved by 4.94 °C and heating energy consumption was reduced by 46.7 %. TRNSYS was utilized to develop the prefabricated house model and the genetic algorithm coupled with MATLAB is employed to perform the optimization calculations by considering the thicknesses of thermal insulation materials on external walls and roofs as variables for optimization. The results indicate that the GA-optimized scheme suggests 300 mm of roof insulation material, 360 mm of floor insulation material, and 110 mm for the north wall as well as 100 mm each for the south, east, and west external walls. Compared to original scheme1, it effectively achieves a reduction in the annual cost value to 6037.09CNY, representing a decrease of 489.853CNY, along with an energy consumption of 9428.36 kWh, reflectiong a reduction by 13.48 %.This study provides experimental data and passive energy-saving optimization solutions for improving thermal performance in retrofitting prefabricated houses.

Authors

Institutions

Publication Details

Journal
Case Studies in Thermal Engineering
Published
2025-04-04
DOI
https://doi.org/10.1016/j.csite.2025.106082
Citations
2
Primary Topic
Building Energy and Comfort Optimization
Type
article
Field-Weighted Citation Impact
2.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Thermal performance and passive energy saving optimization of prefabricated houses in Xinjiang region

Botao Zhou, Rui Liu, Yongqing Fu, Wenjie Zhang et al.
2 citations
Case Studies in Thermal Engineering
Building Energy and Comfort Optimization
2.01
article

Thermal performance and passive energy saving optimization of prefabricated houses in Xinjiang region

Botao Zhou, Rui Liu, Yongqing Fu, Wenjie Zhang, Juan Zhao, Yongcai Li
article en
2 citations

Abstract

Xinjiang uygur autonomous region in China experiences low winter temperatures and strong solar radiation. Actual test results showed that the indoor temperature of a conventional prefabricated house dropped to −10.02 °C at night, resulting in high heating energy consumption of 80.08 kWh. After insulation transformation, the minimum temperature improved by 4.94 °C and heating energy consumption was reduced by 46.7 %. TRNSYS was utilized to develop the prefabricated house model and the genetic algorithm coupled with MATLAB is employed to perform the optimization calculations by considering the thicknesses of thermal insulation materials on external walls and roofs as variables for optimization. The results indicate that the GA-optimized scheme suggests 300 mm of roof insulation material, 360 mm of floor insulation material, and 110 mm for the north wall as well as 100 mm each for the south, east, and west external walls. Compared to original scheme1, it effectively achieves a reduction in the annual cost value to 6037.09CNY, representing a decrease of 489.853CNY, along with an energy consumption of 9428.36 kWh, reflectiong a reduction by 13.48 %.This study provides experimental data and passive energy-saving optimization solutions for improving thermal performance in retrofitting prefabricated houses.

Case Studies in Thermal EngineeringVol. 70
Chongqing University (CN), Xi'an Polytechnic University (CN), Sichuan Provincial Architectural Design and Research Institute (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 16%
Building Energy and Comfort Optimization
2.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.