Integrated Optimization Design Of Building Energy Efficiency Based On Genetic Algorithms

In the context of the energy crisis and environmental issues, building energy consumption occupies a large proportion of energy consumption, with the economic development in our country, the proportion of building energy consumption will continue to increase, building energy conservation situation is grim. There are some problems in the current building energy efficiency design process:the lack of integration of integrated thinking, energy-saving design throughout the building design process lag, etc. Therefore to solve these problems and improve building energy efficiency design effect is without delay. Development of information technology for solving the problem provides new methods and ideas. Based on previous EEB integrated design studies, the article study the EEB integrated optimization design method based on genetic algorithm.By reading a lot of literature related to building energy efficiency work, it summarized the impacting factors of building energy-saving in various stages of building design, including regional climate, building orientation and layout, building form and opening, building materials and building envelope performance, construction technical measures equipment and systems. Based on the previous integration of building integrated design, the principles of design strategy, design phase and measures, the design process and method of the three aspects of building energy-saving integrated optimization methods are discussed and studied. After a new EEB integrated optimization design method formed initially, the EEB integrated optimization design method was researched further for a specific building object of study in the early stages of the construction program. Specific work is as follows: First, building energy consumption, natural lighting and natural ventilation were selected as sub-goals of building energy efficiency for the study of objects; the BIN model, ADF model and PDPH model were selected as predictive model and evaluation; Then the prediction model combined with genetic algorithm with determining the optimization variables and constraints; Real number coding strategy, the selection, crossover recombination, mutation strategy and the maximum evolution generation as the termination condition were determined; integrated optimization design program of BEE (IODPBEE) was written; IODPBEE was used for Guangzhou, Chengdu, Beijing three different thermal district of the city, setting different conditions, the study of architecture optimization variables was optimized; The optimized data calculated used IBM SPSS Statistics 21 for statistical analysis, to verify IODPBEE correctness; At last, IODPBEE be used in integrated optimization design of BEE.In the article, factors affecting building energy efficiency design were analyzed. Based on the study of the existing building energy efficiency and integrated design methods, the genetic algorithm was introduced into building energy-saving integrated design, systematically explored in the building design process in building energy-saving integrated optimization based on genetic algorithm and architectural design preliminary (reduce energy requirements phase) for a specific building object of study, the first application of NSGA-II multi-objective genetic algorithm building energy, natural lighting and natural ventilation for the target application prediction model integrated design, energy-saving integrated optimization based on genetic algorithms buildings deeply substantive research.