Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by us...Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.展开更多
An all-solid-state single-frequency 1064 nm laser with a 100 μs pulse width, 500 Hz repetition rate and 700 m J single pulse energy is designed using seed injection and a three-stage master oscillator power amplifier...An all-solid-state single-frequency 1064 nm laser with a 100 μs pulse width, 500 Hz repetition rate and 700 m J single pulse energy is designed using seed injection and a three-stage master oscillator power amplifier(MOPA) construction.Using this as a basis, research on long-pulse laser frequency doubling is carried out. By designing and optimizing the lithium triborate(LBO) crystal, the theoretically calculated maximum conversion efficiency ηmax reaches 68% at M2=1, while ηminis 33% at M2=3. Generation of 212 m J pulses of green light with a repetition rate as high as500 Hz is obtained from a fundamental energy of 700 m J. The experimental conversion efficiency reaches 31% and the power stability is better than±1%.展开更多
基金The authors received the sources of funding of a project,The Name:Special Project for Innovation and Entrepreneurship Education Reform in Hubei Province Colleges and Universities(2020),Item Number:136/5013602701.
文摘Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.
文摘An all-solid-state single-frequency 1064 nm laser with a 100 μs pulse width, 500 Hz repetition rate and 700 m J single pulse energy is designed using seed injection and a three-stage master oscillator power amplifier(MOPA) construction.Using this as a basis, research on long-pulse laser frequency doubling is carried out. By designing and optimizing the lithium triborate(LBO) crystal, the theoretically calculated maximum conversion efficiency ηmax reaches 68% at M2=1, while ηminis 33% at M2=3. Generation of 212 m J pulses of green light with a repetition rate as high as500 Hz is obtained from a fundamental energy of 700 m J. The experimental conversion efficiency reaches 31% and the power stability is better than±1%.
