Prediction of Airport Energy Consumption Using a Hybrid Grey Neural Network Model

Prediction and control of airport energy consumption plays an important role in promoting energy saving and emission reduction in the civil aviation industry. In view of the complexity and nonlinearity of energy consumption system, as well as a small number of airport energy consumption data, this study develops a hybrid grey neural network model, which organically combines GM (1, 1) model and BP neural network in parallel and series connections, on the basis of analysis of main prediction methods. With energy consumption data from one Chinese airport for the whole year 2010, this study analyzes and compares different prediction results using different models through matlab. It shows that the hybrid model has a better accurate prediction, and its prediction accuracy can be controlled within 7%.

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Neural Network Model Used for Quality Prediction and Control

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)38355-6 ◽

1998 ◽

Vol 31 (29) ◽

pp. 66-68 ◽

Cited By ~ 2

Author(s):

Mika Jarvensivu ◽

Brian Seaworth

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Quality Prediction ◽

Prediction And Control ◽

And Control

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A hybrid neural network model and encoding technique for enhanced classification of energy consumption data

2011 IEEE Power and Energy Society General Meeting ◽

10.1109/pes.2011.6039050 ◽

2011 ◽

Cited By ~ 31

Author(s):

Soma Shekara Sreenadh Reddy Depuru ◽

Lingfeng Wang ◽

Vijay Devabhaktuni ◽

Praneeth Nelapati

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Network Model ◽

Neural Network Model ◽

Hybrid Neural Network ◽

Consumption Data

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Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

JOURNAL OF ADVANCES IN AGRICULTURE ◽

10.24297/jaa.v6i2.5377 ◽

2016 ◽

Vol 6 (2) ◽

pp. 942-952

Author(s):

Xicun ZHU ◽

Zhuoyuan WANG ◽

Lulu GAO ◽

Gengxing ZHAO ◽

Ling WANG

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Shoot Growth ◽

Hyperspectral Data ◽

Estimation Model ◽

Apple Leaves ◽

Bp Neural Network Model ◽

Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

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Prediction of Stock Market by BP Neural Network Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/1744/4/042232 ◽

2021 ◽

Vol 1744 (4) ◽

pp. 042232

Author(s):

Benben Ruan

Keyword(s):

Neural Network ◽

Stock Market ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Bp Neural Network Model

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Regional logistics demand forecasting: a BP neural network approach

Complex & Intelligent Systems ◽

10.1007/s40747-021-00297-x ◽

2021 ◽

Author(s):

Lijuan Huang ◽

Guojie Xie ◽

Wende Zhao ◽

Yan Gu ◽

Yi Huang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Demand Forecasting ◽

Indicator System ◽

Regional Logistics ◽

Logistics Enterprises ◽

Logistics Demand ◽

Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

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Threat Level Forecast for Ship's Oil Spill - Based on BP Neural Network Model

10.1109/cise.2009.5364349 ◽

2009 ◽

Cited By ~ 1

Author(s):

Wenxue Cai ◽

Yanwu Zheng ◽

Yongqiang Shi ◽

Huiling Zhong

Keyword(s):

Neural Network ◽

Network Model ◽

Oil Spill ◽

Neural Network Model ◽

Bp Neural Network ◽

Threat Level ◽

Bp Neural Network Model

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Research on GA-BP neural network model of surface roughness in air drum sanding process for poplar

European Journal of Wood and Wood Products ◽

10.1007/s00107-021-01686-2 ◽

2021 ◽

Author(s):

Xizhi Wu ◽

Han Niu ◽

Xian-Jun Li ◽

Yiqiang Wu

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Bp Neural Network Model

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Misfire Diagnosis of Diesel Engine Based on Short-Time Vibration Characters

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.301 ◽

2010 ◽

Vol 34-35 ◽

pp. 301-305

Author(s):

Zhao Qian Zhu ◽

Jue Yang ◽

Xiao Ming Zhang ◽

Xiao Lei Li

Keyword(s):

Neural Network ◽

Diesel Engine ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Vibration Signal ◽

Vibration Monitoring ◽

Monitoring Test ◽

Bp Neural Network Model ◽

Short Time

This paper studied misfire diagnosis of diesel engine based on short-time vibration characters. Misfire of diesel engine was simulated by the vibration monitoring test. Cylinder vibration signal and top center signal were collected under different states. The short-time vibration signal of each cylinder was intercepted according to the diesel combustion sequence, effective value was calculated, and BP Neural Network model built with this character was used to diagnose diesel misfire. The result shows that this method can locate the misfire cylinder effectively, and it is meaningful for guiding the detection and repair of vehicles.

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