Power load forecasting based on VMD and attention-LSTM

Han Chieh Chao, Fu Lin, Jeng Shyang Pan, Wei Che Chien, Chin Feng Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate forecasting of short-term load forecasting is of great help to demand-side response and power dispatching. In order to improve the accuracy of short-term power load prediction, the original power load data signals are decomposed by using the Variational Mode Decomposition (VMD) method. The decomposed sub-signals and the original signals form a new data set, which is then trained by the neural network. The decomposed sub-signals reflect the detailed features inside the power load that are difficult to be learned by the neural network. Through VMD analysis, the neural network can learn richer information, which is more effective than the superposition prediction method. The neural network prediction model selects an architecture based on Attention-long short term memory (Attention-LSTM). The addition of attention mechanism enables important decomposed information to be fully learned. The effectiveness of this method is proved by experiment.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Data Science and Information Technology, DSIT 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450376044
DOIs
Publication statusPublished - 2020 Jul 24
Event3rd International Conference on Data Science and Information Technology, DSIT 2020 - Xiamen, China
Duration: 2020 Jul 242020 Jul 26

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Data Science and Information Technology, DSIT 2020
CountryChina
CityXiamen
Period20-07-2420-07-26

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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