Existing building multi-objective optimization transformation decision-making method based on GA-RBF algorithm

Abstract

The invention relates to an existing building multi-objective optimization transformation decision-making method based on a GAR-BF algorithm, and the method comprises the following steps: taking the simultaneous minimization of refrigeration energy consumption, thermal comfort and transformation cost as an existing building transformation optimization objective, constructing an objective function,determining the type and number P of decision variables of the existing building multi-objective optimization transformation; determining an optimization interval of each decision variable; determining a reference building and designing an orthogonal table; establishing an existing building performance simulation RBF neural network model; carrying out multi-objective optimization calculation by utilizing an NSGA II algorithm, calling the RBF neural network model by the NSGA II algorithm in each iteration to obtain an output vector matrix so as to update a population of the next iteration, realizing dynamic coupling of the RBF neural network model and the NSGA II algorithm, and obtaining a Pareto optimal solution set; and carrying out statistical analysis on the calculated Pareto optimal solution set to obtain the distribution condition of decision variables, so that the required optimal transformation measures are obtained. Efficiency of actual engineering is improved.

Description

Technical field [0001] The invention relates to an existing building multi-objective optimization transformation decision-making method based on GA-RBF algorithm Background technique [0002] Existing buildings built earlier in the construction period often have poor thermal performance. Simple use of HVAC to improve thermal comfort often fails to achieve the expected results or generates excessive energy consumption, whether from improving the livability level or saving From the perspective of resources, it is necessary to rationally transform existing buildings. However, the renovation of existing buildings is a complex and multi-objective decision-making problem with multiple factors and multiple goals, and it is difficult to intuitively draw the renovation decision. [0003] The key to solving this problem is how to select reform measures and optimization goals and make reasonable and efficient optimization. The optimization of the existing research is only to choose from sev...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the decision-making process of multi-objective optimization and transformation of existing buildings is relatively difficult. Through software simulation, data collection, neural network model establishment, coupling optimization, data analysis and other means, a GA-RBF algorithm is provided. The existing building multi-objective optimization and renovation decision-making method, the method collects the input and output data of the existing building performance simulation, couples the RBF neural network with the NSGA-Ⅱ algorithm, and uses the high-efficiency and high-precision GA-RBF multi-objective optimization The algorithm obtains the Pareto optimality of the decision variables, and performs statistical analysis on the Pareto optimal solution set to obtain the optimal transformation measures

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