Prediction of GDP Carbon Emission Based on Grey Model and Neural Network

2021 ◽  
pp. 43-55
Author(s):  
Feng Zhang ◽  
Huihuang Zhao ◽  
Manimaran Ramasamy
Keyword(s):  
Neural Network ◽  
Carbon Emission ◽  
Grey Model

scholarly journals A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7559
Author(s):  
Lisha Li ◽  
Shuming Yuan ◽  
Yue Teng ◽  
Jing Shao
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Influencing Factors ◽  
Carbon Emission ◽  
Estimation Method ◽  
Civil Aviation ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Fuel Price ◽  
Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

scholarly journals Predicting air pollutant emissions from a medical incinerator using grey model and neural network

2015 ◽  
Vol 39 (5-6) ◽  
pp. 1513-1525 ◽  
Author(s):  
Tzu-Yi Pai ◽  
Huang-Mu Lo ◽  
Terng-Jou Wan ◽  
Li Chen ◽  
Pei-Shan Hung ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Grey Model

Long-Term Sales Forecast Method Based on Grey Model and Artificial Neural Network

2013 ◽  
Vol 385-386 ◽  
pp. 1726-1729
Author(s):  
Yi Jun Wang ◽  
Hong Ying Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simulation Experiment ◽  
Grey Model ◽  
Forecast Method ◽  
Sales Forecast ◽  
Long Time ◽  
Artificial Neural ◽  
The U.S

Long-term sales forecasting is a problem that has been focused on for a long time. In order to forecast the long-term sales of an industry or an enterprise accurately, a new method based on Grey Model and Artificial Neural Network is proposed in this paper. The effectiveness and feasibility of the proposed method is verified by simulation experiment using sales data of the manufacturing and trade industry provided by the U.S. government.

Wind Speed Forecasting Based on Variable Weight Combination Model of Neural Network and Grey Model

2012 ◽  
Vol 217-219 ◽  
pp. 2654-2657
Author(s):  
Jian Zhang ◽  
Lun Nong Tan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Wind Farms ◽  
Grey Model ◽  
Weighting Coefficient ◽  
Forecasting Accuracy ◽  
Wind Speed Forecasting ◽  
Variable Weight ◽  
Artificial Neural Network Ann

The wind speed forecasting accuracy of artificial neural network(ANN) and grey model(GM) is poorly satisfied. Thus, we proposed a new variable weight combined (VWC) model, which was based on the ANN and GM, to improve the wind speed forecasting accuracy. VWC used weighting coefficient of different time to fit the two single models. The forecasting accuracy of VWC is higher than either of the two single models, and is also higher than the unchanged weight combination(UWC) model. Our data show a new method for wind speed forecasting and the reduction of auxiliary service costs of wind farms.

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