Open AccessResearch on Power Load Forecast Based on Ceemdan Optimization Algorithm
Author(s) -
Huang Lilong,
Chao Zhang,
Ping Yu
Publication year - 2020
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 - 10.1088/1742-6596/1634/1/012142
Subject(s) - aliasing , algorithm , artificial neural network , computer science , modal , power (physics) , process (computing) , limiting , data mining , artificial intelligence , engineering , quantum mechanics , mechanical engineering , chemistry , physics , undersampling , polymer chemistry , operating system
With the continuous development of the power industry, power data has become more complex. However, it is difficult for common shallow neural networks to fully extract the original load data features, thus greatly limiting the load prediction accuracy. Therefore, given the advantages of CEEMDAN, a prediction model based on CEEMDAN is proposed in the paper, the advantages of which are verified by simulation analysis of measured power data. Experiments prove that the algorithm proposed in the paper has higher prediction accuracy, which effectively overcomes the EMD modal aliasing problem, and the decomposition process is more complete, so that the prediction accuracy of the subsequent prediction model is improved.