Short-term Power Load Forecasting Based on LSTM Neural Network Optimized by Improved PSO

Authors

Tengfei Wei, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
Tinglong Pan, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: To improve the accuracy of short-term power load forecasting, a short-term power load forecasting model (ACMPSO-LSTM) based on long-short memory neural network (LSTM) optimized by adaptive Cauchy mutation particle swarm optimization (ACMPSO) is proposed. For the problem of difficult selection of LSTM model parameters, ACMPSO is used to optimize model parameters, and non-linear changing inertia weights are adopted to improve the global optimization ability and convergence speed of PSO algorithm. In the optimization process, a mutation operation based on genetic algorithm is added to reduce the risk of particles falling into local optimal solutions. The simulation results show that the ACMPSO algorithm for LSTM can effectively improve the accuracy and stability of short-term power load forecasting.

Recommended Citation

Wei, Tengfei and Pan, Tinglong (2021) "Short-term Power Load Forecasting Based on LSTM Neural Network Optimized by Improved PSO," Journal of System Simulation: Vol. 33: Iss. 8, Article 14.
DOI: 10.16182/j.issn1004731x.joss.20-0297
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss8/14

First Page

1866

Revised Date

2020-07-28

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0297

Last Page

1874

CLC

TP391.9

Recommended Citation

Wei Tengfei, Pan Tinglong. Short-term Power Load Forecasting Based on LSTM Neural Network Optimized by Improved PSO[J]. Journal of System Simulation, 2021, 33(8): 1866-1874.

DOI

10.16182/j.issn1004731x.joss.20-0297