Open AccessApplication of Particle Swarm Optimization Combined with Long and Short-term Memory Networks for Short-term Load Forecasting
Author(s) -
Mingchong Han,
Aiguo Tan,
Zhong Jin
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 - 10.1088/1742-6596/2203/1/012047
Subject(s) - particle swarm optimization , term (time) , computer science , long short term memory , reliability (semiconductor) , artificial neural network , artificial intelligence , multi swarm optimization , recurrent neural network , machine learning , power (physics) , physics , quantum mechanics
In this paper, we apply the Long Short-Term Memory (LSTM) network to short-term load forecasting, and use the TensorFlow deep learning framework to build a Particle Swarm Optimization (PSO) model to optimize the parameters of the LSTM. Optimization (PSO) model to optimize the parameters of LSTM. In this paper, we use the meteorological data and historical load data of a certain place as the input of LSTM before and after optimization, and compare the model with the BP Neural Network before and after optimization, and the results show that the PSO-LSTM model has higher reliability and prediction accuracy.