APSO-LSTM model based on improved Tent mapping for natural gas demand forecasting

APSO-LSTM model based on improved Tent mapping for natural gas demand forecasting

In order to accurately forecast the change of natural gas demand under the condition of multi-dimensional influencing factors, the adaptive inertia weight factor was introduced to improve the Particle Swarm Optimization (PSO) algorithm. Then, by combining the constructed Adaptive Particle Swarm Opti...

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Bibliographic Details
Main Authors: WEN Quan, SHI Xiaoping, ZHANG Lifen, LI Lei
Format: Article
Language:zho
Published: Editorial Office of Oil & Gas Storage and Transportation 2023-06-01
Series:You-qi chuyun
Subjects:
natural gas demand
tent chaotic mapping
apso-lstm
pso-lstm
lstm
bp neural network
forecasting
Online Access:http://kykxxb.cumtb.edu.cn/article/10.6047/j.issn.1000-8241.2023.06.012

Internet

http://kykxxb.cumtb.edu.cn/article/10.6047/j.issn.1000-8241.2023.06.012

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