Abstract
Aiming at the poor efficiency of traditional PSO algorithm in energy consumption optimization of office buildings in cold areas, a building energy consumption optimization method based on improved PSO was proposed. Firstly, the basic principle of PSO algorithm is analyzed, and then the PSO algorithm is improved by GE or AG operators. On the basis of the improvement, different test functions were used to optimize the algorithm, and then the maximum PSO parameters were verified. Finally, the above improvements are applied to a building example. The results show that the improved PSO energy consumption optimization method can not only improve the fast travel performance of the algorithm, but also reduce the overall search time.
Highlights
At present, the optimization of building energy consumption at home and abroad mainly adopts PSO, genetic algorithm and differential evolution algorithm, and has carried out a lot of academic research, such as Gaoyuelin ( 2020 ) differential evolution algorithm to optimize the energy consumption of buildings, which improves the optimization ability of some algorithms, but it is easy to fall into local optimum
Aiming at the poor efficiency of traditional PSO algorithm in energy consumption optimization of office buildings in cold areas, a building energy consumption optimization method based on improved PSO was proposed
The results show that the improved PSO energy consumption optimization method can improve the fast travel performance of the algorithm, and reduce the overall search time
Summary
The optimization of building energy consumption at home and abroad mainly adopts PSO, genetic algorithm and differential evolution algorithm, and has carried out a lot of academic research, such as Gaoyuelin ( 2020 ) differential evolution algorithm to optimize the energy consumption of buildings, which improves the optimization ability of some algorithms, but it is easy to fall into local optimum. In the iterative operation, each particle will be updated according to two optimal values: first, in the process of successive iterations, the optimal solution of the position of the particle is denoted as Pbest ; The second is to find the global optimal position among all particles, referred to as the global optimal denoted as gbest. In this articler,the traditional PSO algorithm is improved, which is mainly optimized by GA or DE operators. The operation will retain particles that generally meet the requirements, and the other half will be re-discharged to the complete search space, so as to increase the global search ability of the algorithm
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