The Research of Oil & Gas Energy Saving Index Prediction Based on Neural Network and Quantum-Behaved Particle Swarm Optimization

Abstract:

This paper introduces method of gas energy saving target forecast that a quantum particle swarm optimization algorithm and BP neural network, using BP neural networks and quantum particle swarm global search ability strong advantage, through the method that improved average optimal position. It solved the BP neural network is being trapped in local minima and slow convergence speed problem. It realized target forecast that based on BP neural network and quantum particle swarm field energy saving. With the production water injection pump unit consumption data for training data, prediction results show that this method can achieve good prediction effect. It can be applied to practice.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 3550-3554
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.3550
• Online since: August 2013
• Authors: Ji Sheng Liang, Hui Yang, Zi Bo Yuan, Hui Wang
• Keywords: Algorithm Optimization, BP Neural Network (BPNN), Energy Saving of Gas, Energy Saving of Oil, Quantum Partical Swarm

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DOI: 10.7498/aps.59.3686