Research On Energy Consumption Forecast And Energy Efficiency Improvement Roadmap Of Urban Public Buildings In China

In order to effectively alleviate the contradiction between economic and social development and energy and environment capacity,seeking sustainable development,a new mechanism of dual-controlling both the energy consumption intensity and the total energy consumption has been put on the agenda.As Public building sector is one of the key areas which largely influences the achievement of energy conservation and emission reduction targets of not only the building sector but also the entire country.Therefore,to clear the energy consumption status and predict its growth trend in public building sector,as well as to explore the energy efficiency improvement roadmap are of great significance for guiding energy efficiency improvement,controlling energy consumption growth rate and realizing energy conservation and emission reduction goal in public building sector.On the background of China's rapid urbanization,by using a series of qualitative and quantitative research methods,this dissertation takes public buildings in China's urban area as the research object and conducts an in-depth research on its macro terminal energy consumption and energy efficiency issues.Firstly,based on the index method and statistical yearbook data splitting method,this dissertation calculates and analyzes the total energy consumption and the overall energy consumption intensity of urban public buildings in China.Secondly,using the nonlinear regression analysis model,trend extrapolation method,and system verification method,China's urban public building energy consumption prediction model is developed and urban public buildings' energy consumption growth trend is forecasted.Thirdly,with the vision traction method,institutional questionnaire method and Delphi method,China's urban public buildings' energy efficiency improvement roadmap is designed.And based on the key parameters of the roadmap,the roadmap's energy saving is calculated by constructing a TAYLOR series BP neural network model.Finally,the internal structure and path distribution of public building energy efficiency improvement performance influencing factors' are explored by using EFA and SEM methods.Based on that,a series of safeguard measures and suggestions for China's urban public buildings energy efficiency improvements and proposed.The innovation of this dissertation is mainly reflected in the following 3 aspects:(1)The nonlinear regression analysis method is used to construct the energy consumption prediction model of urban public buildings in China.Based on the calculation of China's urban public building energy consumption and energy intensity on the status quo,through theoretical analysis,the energy consumption forecast theory model of China's urban public building is developed.After the establishment of nonlinear regression model equations and the model test using MATLAB and DATEFIT,the independent variables are predictively assigned and intermediary variables are checked and judged by using the trend extrapolation method and system verification method respectively,and then China's urban public building energy consumption growth trend through 2015 to 2030 is predicted.(2)An energy efficiency improvement roadmap for urban public buildings in China is designed by using the vision traction method and the roadmap's energy saving is calculated.With the mechanism of vision traction method,through institutional questionnaire survey and improved Delphi method,a medium-and-long-term(2016-2030)energy efficiency improvement roadmap for urban public buildings in China is developed.The roadmap includes the gradient targets system of energy efficiency improvement of both new construction public buildings but also existing public buildings.Also,it contains the indicator systems which describing the status of public building energy efficiency in year 2020 and 2030,respectively.Based on the key parameters of the roadmap,the roadmap's energy saving is calculated by constructing a TAYLOR series BP neural network prediction model.(3)By using EFA and SEM,the internal structure and path distribution of public building energy efficiency improvement performance influencing factors' are explored and based on which a series of safeguard measures for China's urban public buildings energy efficiency improvements and proposed.Firstly,according to the literature review and expert interviews,the factors which influence the public building energy efficiency improvement performance is identified and a factor list is developed.Then,by using EFA and SEM methods,with the assistant of SPSS and AMOS software tools,the internal structure and path distribution of performance influencing factors is further explored.Lastly,puts forward a series of safeguard measures and suggestions for China's urban public buildings energy efficiency improvements and proposed.