Research On Energy Efficiency Evaluation Of Prefabricated Buildings In Hot Summer And Cold Winter Regions Based On BP Neural Network

As the total energy consumption of all countries in the world is increasing,energy consumption has now become the focus of increasing attention of all countries.Under the overall demand of increasing total energy consumption,how to solve the problem of increasing energy demand and reduce energy consumption as much as possible,thereby reducing carbon dioxide emissions due to energy consumption Such issues have become the focus of research in various countries in the past century.Decades of research and practice have shown that building energy conservation is currently the most effective,one of the effective ways to improve energy efficiency and reduce carbon emissions.Building energy efficiency evaluation is a way to transform the concept of energy conservation into technical means.In the research process,the development of relevant policies through investigations is firstly understood,and key cities are selected to understand the development of prefabricated structures.So the effect of buildings that adopt prefabricated construction methods in energy conservation can be assessed.As our country vigorously promotes prefabricated buildings,the area and application scope of prefabricated buildings will further grow rapidly.It is of great significance to establish an energy-saving evaluation system with certain reference significance.So establishing a building energy efficiency evaluation model is meaningful.This paper set up an energy efficiency evaluation system of prefabricated buildings in hot summer and cold winter regions.Firstly,by searching standards and literature,studying the literature of relevant domestic scholars,screening the commonly used building energy efficiency evaluation indicators,evaluation indicators are primarily selected.Secondly,the selected evaluation indicators are further analyzed and determined.Thirdly,an energy efficiency evaluation model is set up using the determined evaluation indicators through the analytic hierarchy process.Fourthly,expert questionnaire surveys determine the index weight which are modified based on the principle of information entropy.Fifthly,energy efficiency grades are determined.Through example analysis,based on the comparison between the results from the evaluation model and the results of " Energy Efficiency Building Evaluation Standards",the applicability of the evaluation model is verified.Next,the evaluation model established in this article is used to evaluate the energy efficiency of 18 prefabricated buildings in the hot summer and cold winter area in Hubei.Finally,the BP neural network is applied to this evaluation model.And 18 prefabricated buildings are used as samples to perform the BP neural network evaluation.The effect is good.It shows that the evaluation based on BP network is feasible and effective.The main research results of this paper include: First,by studying the energy-saving characteristics of prefabricated buildings,determining the use of assembly rate,component installation level and full decoration level and other indicators to express the energy-saving characteristics of prefabricated buildings,which is convenient for constructing models;second,constructing prefabricated buildings The index system of building energy efficiency evaluation includes five major aspects including assembly index,building planning,envelope structure performance,building energy efficiency and equipment energy efficiency,environment and operation management,including 18 second-level indexes;the third is the construction of prefabricated The building energy efficiency evaluation model has determined the weight coefficients of each index and revised the weights;fourth,the energy efficiency evaluation grades of prefabricated buildings are divided into four different energy efficiency grades: excellent,good,medium,and poor;Fifthly,it is verified by examples.The correctness of the evaluation model is verified by comparison with the evaluation results of the "Evaluation Standards for Energy-saving Buildings";the sixth is the establishment of an energy-saving evaluation model based on BP neural network,and the application value is analyzed.This research can provide decision-making reference for the energy efficiency evaluation of prefabricated buildings in hot summer and cold winter areas and follow-up energy saving research.