The Low Energy-Consumption Strategy For Improving Indoor Thermal Environment Quality In Residential Building

With the rapid development of national residential construction, remarkable improvement of social economy and the increasing need of residents to get healthy and comfortable indoor environment in recent decades, energy efficiency now is especially urgent. How to make full use of natural resources and all kinds of climate adaptability control strategies on the premise of the acceptable thermal comfort of occupants and eventually coming up with low energy consumption control strategies for residential building has become one of the difficulties for energy efficiency. It has very important and profound significance to improve residents'living quality, reduce energy consumption and promote sustainable development for habitat environment by finding out ways and methods to improve indoor thermal environment, studying climate adaptability control techniques and design strategies and making prior use of natural resources.Energy consumption and indoor thermal comfort are the two basic conflicting factors for building design. In order to keep indoor thermal comfort and reduce energy consumption at the same time, all these factors have to be made an overall and multiple targets optimizing. Therefore this paper has firstly made annual residential building energy consumption and indoor adaptability comfort as the goal of evaluated performance. And the paper chooses prediction model founded by artificial neural network as the fitness function and establishes multiple targets optimizing model of building design program on the foundation of genetic algorithm NAGAâ…¡.Every design scheme needs assessment function to evaluate residential building energy consumption and indoor comfort when using genetic algorithm on the process of multiple targets optimizing. And the time consumed in thousands of iterative computations is incalculable when using the conventional dynamic simulation method. Thus, this paper has taken the artificial neural network model of annual energy consumption and indoor comfort as the adaptability assessment function, discussed the issue that BP artificial neural network is easy to get into the problem of partial optimum solution, optimized the connection right of BP artificial neural network based on genetic algorithm and got the more accurate and practical GA-BP neural network used for prediction of annual residential building energy consumption and indoor comfort, determined the input neural parameters of GA-BP model including building floor plan, location, shape coefficient and other 11 variables and the output neural parameters are annual energy consumption per unit area and indoor comfort conditions.Secondly, based on the aPMV thermal comfort control model proposed by automation control black box theory and numerous researched data, the paper defines the comfort zone of residential building in Chongqing, which solves a fundamental problem in the study of design and strategy control and gives a limitation to indoor adaptability thermal comfort. The fundamental problem is what kind of thermal comfort should be fulfilled when studying the low energy consumption strategy.Then this research chose a typical building in Chongqing making the acceptable comfort zone of residential building as the limited condition and analyzed the influence of different kinds of design parameters on indoor comfort in natural environment with numerous simulations. At the same time, it also analyzed the influences of all kinds of design factors on annual energy consumption and proposed design suggestions of energy efficiency for every single design parameters. On this basis, 144 groups of simulation data are provided for trainings of GA-BP multiple targets prediction model.The prediction model of residential building energy consumption and indoor comfort was trained and tested with the numerical simulation data of Energy Plus. And the tested data showed that this model which had certain application value could well predict the energy consumption and indoor comfort of residential buildings.After making GA-BP neural network model of residential building energy consumption and indoor comfort as the adaptable assessment function, the paper made a case study of multiple targets optimization design towards basic building scheme, and then came up with an energy-saving design scheme which could keep the indoor environment thermal comfort. The multiple targets optimization design model has a relatively accuracy by comparing and analyzing its results and the results of the simulation software for energy consumption, which shows that the multiple targets optimization design model can guide architectures to make scheme design.Finally, on the basis of energy efficiency optimization design program and with the limitation of acceptable comfort zone, this paper gets the monthly control methods of ventilation and shading technology in residential buildings in Chongqing by using state space method. After getting the typical passive control methods, the author measured the thermal environment in a building of Baisha Town, Jiangjin District in Chongqing and finally verified its effectiveness in real residential buildings. The results of measurement show that the typical passive control methods raised in this paper have certain scientific value and practicality.The purpose of the thesis is to provide specific analytical method and practical tool for the requirement of thermal comfort for building design. And on the base of energy consumption optimization, this thesis has raised control methods for building management used for specific climate and human comfort which achieved the combination of passive and active technology, found out a low energy consumption control strategy fit for residential buildings in Chongqing, and provided technical support for the establishment of building energy-saving technology system in Chongqing.