Building energy consumption management system based on particle swarm algorithm optimization
As a promising countermeasure to the problem that traditional BP neural networks are prone to getting stuck in local optima when predicting building energy consumption,an improved PSO (particle swarm optimization)-BP neural network was proposed for the prediction of building energy consumption management system. Firstly,DeST (designer's simulation toolki) was used to construct the building maintenance structure and simulate the air conditioning energy consumption output under different meteorological parameter conditions. The simulated sample data was divided into training set samples and testing set samples. Then the PSO-BP neural network algorithm was used to train and learn the training set and obtain an empirical formula for energy con-sumption prediction. To facilitate the practical application of the algorithm,the formula was encapsulated in the backend code in Java. Finally,the test set samples were input into the formula to verify the accuracy of the equation. The results show that,com-pared with the simulation results,the error percentage of PSO-BP energy consumption prediction is between[-1.110％,1. 167％].
building energy consumptionDeST simulationPSO-BP neural networkJava language
万方数据
维普
