Adaptive neural network prediction model for energy consumption

2011 ◽  
Author(s):  
Maryam Jamela Ismail ◽  
Rosdiazli Ibrahim ◽  
Idris Ismail
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Adaptive Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction

Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network

2020 ◽  
Vol 305 ◽  
pp. 163-168
Author(s):  
Peng Gu ◽  
Chuan Min Zhu ◽  
Yin Yue Wu ◽  
Andrea Mura
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Swarm Optimization ◽  
Al Composite ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

scholarly journals Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Dongxiao Niu ◽  
Yanan Wei ◽  
Yanchao Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Power Prediction ◽  
Adaptive Neural Network ◽  
Scene Simulation ◽  
Knowledge Mining ◽  
Photovoltaic Power ◽  
Model Combining ◽  
Neural Network Prediction ◽  
Network Prediction

Influenced by light, temperature, atmospheric pressure, and some other random factors, photovoltaic power has characteristics of volatility and intermittent. Accurately forecasting photovoltaic power can effectively improve security and stability of power grid system. The paper comprehensively analyzes influence of light intensity, day type, temperature, and season on photovoltaic power. According to the proposed scene simulation knowledge mining (SSKM) technique, the influencing factors are clustered and fused into prediction model. Combining adaptive algorithm with neural network, adaptive neural network prediction model is established. Actual numerical example verifies the effectiveness and applicability of the proposed photovoltaic power prediction model based on scene simulation knowledge mining and adaptive neural network.

scholarly journals PTZ Control System of Indoor Rail Inspection Robot Based on Neural Network Prediction Model

2017 ◽  
Vol 107 ◽  
pp. 206-211 ◽  
Author(s):  
Zhiyuan Liu ◽  
Xinyang Zhao ◽  
Jichao Sui ◽  
Hongli Wang ◽  
Yongcheng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Prediction Model ◽  
Inspection Robot ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Rail Inspection
Sign in / Sign up

Export Citation Format

Share Document