Thermal storage performance analysis of building envelope based on big data sensor network

Abstract

In order to understand the thermal storage performance analysis of building envelope, the author proposes a research on thermal storage performance analysis of building envelope based on Big data sensor network. The author first compares and analyzes the thermal storage performance parameter systems of different theoretical methods, and determines the key characterization parameters of thermal storage performance under different working conditions based on the analysis of the thermal storage process. Second, in order to compare and verify the measured and simulated indoor temperature of the building, two groups of buildings with identical exterior wall insulation performance and distinct thermal storage performance were chosen. Three distinct thermal storage levels of the building?s exterior walls were used to simulate and analyze the internal surface temperature, heat flux density, and building cooling and heating load. Summer night ventilation was used to investigate the effect of ventilation volume on building cooling loads. In the end, a provincial office building was chosen as the research subject. The structure comprises of two stories, one underground and two over the ground, with a north hub point of 180?. The investigation object is a two-story room. The trial results demonstrate that the deliberate outcomes are in great concurrence with the mimicked results. For the district of the region, under a similar protection execution of the nook structure, the inner surface temperature of the weighty construction outside wall is higher in winter, lower in summer, with a higher intensity load in winter and a lower cooling load in summer. The proper air changes each hour of building night ventilation is 16 ach, and the effect on the cooling heap of weighty structures is huge. Demonstrated the precision of the reproduction technique and model.