Open AccessImproving prediction accuracy of cooling load using EMD, PSR and RBFNN
Author(s) -
Limin Shen,
Yuzhen Wen,
Xiaohong Li
Publication year - 2017
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/887/1/012016
Subject(s) - hilbert–huang transform , residual , computer science , cooling load , component (thermodynamics) , mode (computer interface) , chaotic , process (computing) , phase (matter) , phase space , function (biology) , algorithm , control theory (sociology) , pattern recognition (psychology) , artificial intelligence , engineering , physics , mechanical engineering , control (management) , filter (signal processing) , quantum mechanics , air conditioning , evolutionary biology , biology , computer vision , thermodynamics , operating system
To increase the accuracy for the prediction of cooling load demand, this work presents an EMD (empirical mode decomposition)-PSR (phase space reconstruction) based RBFNN (radial basis function neural networks) method. Firstly, analyzed the chaotic nature of the real cooling load demand, transformed the non-stationary cooling load historical data into several stationary intrinsic mode functions (IMFs) by using EMD. Secondly, compared the RBFNN prediction accuracies of each IMFs and proposed an IMF combining scheme that is combine the lower-frequency components (called IMF4-IMF6 combined) while keep the higher frequency component (IMF1, IMF2, IMF3) and the residual unchanged. Thirdly, reconstruct phase space for each combined components separately, process the highest frequency component (IMF1) by differential method and predict with RBFNN in the reconstructed phase spaces. Real cooling load data of a centralized ice storage cooling systems in Guangzhou are used for simulation. The results show that the proposed hybrid method outperforms the traditional methods.