Research On Building Energy Optimization Method Based On Dynamic Energy-saving Contribution Rate

At present,the main method for building energy-saving optimization is to modify design parameters one by one and run the energy simulation for verification.Therefore,the relationships between parameters and energy consumption and parameters which have great impact on energy consumption are the keys to guide energy-saving optimization design efficiently.However,most of the existing analysis methods use simulation software to analyze standard building model,so that the applicability of the results obtained is poor,it can’t meet the actual optimization requirements.Therefore,the improvement of the existing analysis method has great significance for guiding the building energy-saving optimization process.At the same time,the existing method can only provide static guidance suggestions,it can’t be dynamically integrated with the energy-saving optimization process.Therefore,research and development based on dynamic analysis optimization methods and tools of energy-saving contribution rate(ECR)would have great significance for realizing the dynamic and intelligent energy saving optimization process.The specific research contents of this study are as follows:First of all,the existing ECR calculation method was improved by energy consumption surrogate model and variable strategy of the benchmark building.In the process of energy-saving optimization,the improved calculation method was used for dynamic ECR analysis,the parameter and step size to optimize would be selected by greedy strategy.Thus,the optimization algorithm based on dynamic energy-saving contribution rate(DECR)was proposed.Based on a case study of high-speed railway station,a group of optimization experiments were carried out.The results showed that comparing with the existing ECR calculation method,optimization results of DECR algorithm is reduced by 8.04 k Wh/m~2 per generation on average.Compared to PSO algorithm,the energy consumption of DECR algorithm is reduced by 1.3%,and the convergence rate is increased by 15%.It is proved that DECR algorithm is effective.Secondly,greedy strategy of the DECR optimization algorithm was replaced by the human-machine interaction suggestion generation mechanism,so that the optimization process was discretized.With the optimization suggestions provided in each step of optimization,users can select parameter and step size to optimize independently.Then the dynamic energy-saving contribution rate based decision-making optimization algorithm(DODM)was proposed.A group of optimization experiment was carried out through the case study of high-speed railway station.The results showed that under the same optimization goals,program modification rate(PMR)of DODM algorithm is 51.2%lower than PSO algorithm;when under same PMR targets,the optimization result of DODM algorithm is 11.3%lower than PSO algorithm.This proved the effectiveness of DODM algorithm.Finally,DODM algorithm was loaded into Grasshopper by Python,the user interface was written by Human UI plug-in,and the building model was displayed by Rhino.Thus,the building energy optimization system based on the dynamic energy-saving contribution rate(DBOS)has been developed.20 testers were invited to test the DBOS system,the results show that DBOS system developed in this study can help users reduce optimization modification times by 57.7%compared to the case without optimization tools.This proved the effectiveness of DBOS system.