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

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

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.

Key words: short-term power load forecasting, particle swarm optimization, long short term memory neural network, inertia weight, mutation operation

CLC Number:

TP391.9

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.

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