Open AccessShort-term Impact Load Forecasting Model Based on Multi-objective Antlion Optimization Algorithm
Author(s) -
Jing Chen,
Aiguo Tan,
Zhong Jin,
Mingchong Han
Publication year - 2022
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 - 10.1088/1742-6596/2203/1/012074
Subject(s) - extreme learning machine , term (time) , computer science , hilbert–huang transform , stability (learning theory) , set (abstract data type) , mode (computer interface) , decomposition , algorithm , optimization algorithm , data set , glitch , series (stratigraphy) , data mining , artificial intelligence , machine learning , mathematical optimization , artificial neural network , mathematics , ecology , telecommunications , paleontology , physics , filter (signal processing) , quantum mechanics , detector , computer vision , biology , programming language , operating system
In this study, a novel multi-objective antlion optimization (MOALO) algorithm-based impact load prediction model is proposed to address forecasting problem in the area with a lot of impact load. To reduce the impact of glitch in impact load data, ensemble empirical mode decomposition(EEMD) is applied to decompose the primitive load data into a set of sub-layers. Then, a novel MOALO-based extreme learning machine (ELM) forecasting model is put forward to make short-term prediction by using the decomposed sub-series. Finally, superimpose the prediction results. According to case study, the proposed EEMD-MOALO-ELM model has the best prediction accuracy and stability.