Multi-objective optimization of building planning energy saving based on genetic algorithm

Abstract

The construction industry itself has led to a large amount of energy consumption, and this paper is based on genetic algorithm to optimize the planning and design of rural buildings with energy saving and multi-objective. Firstly, the multi-objective optimization problem and Pareto concept are discussed, the NSGA-II algorithm is applied to solve the problem of building energy saving integrated optimization, and the algorithm implementation of building energy saving integrated optimization design based on NSGA-II algorithm is given. Use incremental costs and incremental benefits as objective functions and make relevant assumptions to keep the model realistic while simplifying calculations. Second, a series of constraint functions are set up to ensure the superiority of the results. By constructing model assumptions, objective functions and constraint functions, the energy-saving optimization model is formed and solved by genetic algorithm. From the optimization results, it can be seen that the energy consumption of all the design schemes in the Pareto solution set is between 25.0 KWh/m2∼31.7 KWh/m2. Therefore, in the selection of architectural planning and design schemes, we should avoid simply pursuing low energy consumption and high comfort, and also use comprehensive consideration of economic costs to choose the most cost-effective scheme.