Short-term Building Cooling Load Prediction Model Based on Improved Bald Eagle Search Algorithm for Optimizing Gated Recurrent Unit
It plays an important role to accurately predict the cooling load of buildings in energy-saving optimization control of air conditioning systems,therefore,an improved bald eagle search(BES)algorithm is proposed to optimize the short-term cooling load prediction model of gated recirculation units(GRU);Firstly,the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm is used to decompose the building cooling load data into components with different frequencies,and the random forest combined with the recursive feature elimination method is used to select the corresponding features for the components with different frequencies;Finally,the improved BES algorithm is used to optimize the parameters of the GRU model.With the shortcomings of the BES algorithm,the Sobol sequence is introduced to initialize the population,adopting the nonlinear control factor to balance the search capability of the BES algorithm,and the adaptive t-distribution strategy to enhance the algorithm's optimization ability;Experimental results show that compared with the GRU and GRU with improved BES algorithm,the proposed prediction model decreases the root mean square error by 34.27,22.41,the average percentage error by 2.72％,2.63％,and the average abso-lute error by 27.25,25.26;Compared with other prediction models,the proposed prediction model has a higher prediction accuracy,which is more advantageous in practical engineering applications.
万方数据
维普
