Open AccessPower Load Forecasting Model Based on Deep Neural Network
Author(s) -
Jian Yuan,
Jiaying Wang,
Qing Cheng,
Jiaxing Sun
Publication year - 2021
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 - 10.1088/1742-6596/1852/3/032010
Subject(s) - particle swarm optimization , artificial neural network , computer science , power (physics) , dual (grammatical number) , swarm behaviour , artificial intelligence , algorithm , art , physics , literature , quantum mechanics
Aiming at the problems of the traditional power load forecasting model, based on the analysis of the traditional DNN neural network model, a PSO dual improvement and optimization power load forecasting model is proposed. In this model, the discrete particle swarm algorithm is used to determine the DNN network architecture, and then the particle swarm algorithm is used to optimize the parameters of the neural network to obtain a model with the best structure and parameters. Finally, it is verified by simulation, and the results show that the above method is feasible and has high prediction accuracy.