Study On Residential Building Thermal Environment And Energy Prediction For Small Towns In Chongqing

The construction of towns has been developing rapidly, but the research about the indoor environment in small towns has been limited. And with the economic development in small towns, the living condition is improved and the people also require more on the indoor environment , thus, the research of residential indoor environment of the small towns are becoming more important. It is needed to improve its indoor environment with the low energy consumption according to the characteristics of small town.In the early stages of building design, many factors are not determined and It is difficult to analyse heating and cooling loads using the building energy softwares. So it is urgent and significant to find a user-friendly method of analysis which can be used to analyse architectural design factors influencing loads and select the energy conservation strategies.Based on the typical city of hot in summer and cold in winter climate zone-----Chongqing, this research takes the small town residential building for studying. Considering the local conditions of Chongqing small town , including the society, the economy, the environment and the resource, the energy use and the thermal environment in Baisha town's residential building has been investigated. The residential building thermal environment of small town in Chongqing has also been analyzed based on the residential building's characteristics.The factors influencing residential building load are analyzed. The factors studied are selected according to the basic situation of Chongqing small town. The experiment is arranged using the orthogonal experiment method. In view of the advance of the building energy computer simulation technology in the building energy efficiency , DeST, an energy dynamic computation software is chosen to carry on the computer simulation experiments for the studied building.Using the MATLAB software, the simulation results are regressed as the multi-dimensional linear regressions method. Three multiple linear regression equations are achieved, which express the relationships between the cooling load,the heating load, the total load of the whole year and the studied factors. The error analysis is done with many kinds of methods. It is proved that the residential building loads forecast equations for Chongqing small towns obtained in this research are not only easy to use...