Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China

A new multivariable grey prediction model with structure compatibility

Improved multi-variable grey forecasting model with a dynamic background-value coefficient and its application

Forecasting Chinese CO 2 emissions from fuel combustion using a novel grey multivariable model

Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives

An improved grey multivariable model for predicting industrial energy consumption in China

Study of the influence mechanism of China's electricity consumption based on multi-period ST-LMDI model

Changes in economic development stage and growth type will lead to variations in the CO2 emissions. Traditional empirical analysis of the variations often only considers the impact of influencing factors on CO2 emissions from a single dimension. Under the background of China’s economy transferring from high-speed growth to high-quality development, this paper comprehensively considers the characteristics of the relevant influencing factors under different development stages and growth rates, and further calculates the panel gray incidence degree between CO2 emissions and these influencing factors in eastern, central, and western China. Based on the different development conditions, corresponding benchmarks of the indicators for the three regions (eastern, western, and central China) are accordingly set, highlighting the unity as well as the uniqueness between different regions. Furthermore, this paper verifies the environmental Kuznets curve (EKC) in the three regions. The result shows that all the factors of per capita Gross Domestic Product (GDP), Energy Intensity, Urbanization Level, and Trade Openness have a high correlation with CO2 emissions in the three regions, in which CO2 emissions are all between the two inflection points of the inverted N-shaped model.

https://www.mdpi.com/1660-4601/16/6/944/pdf

Analysis of the Influence Mechanism of CO2 Emissions and Verification of the Environmental Kuznets Curve in China

Exploring the influencing factors of urban residential electricity consumption in China

Significant wave height (SWH) prediction plays an important role in marine engineering fields such as fishery, exploration, power generation, and ocean transportation. Traditional SWH prediction methods based on numerical models cannot achieve high accuracy. In addition, the current SWH prediction methods are largely limited to single-point SWH prediction, without considering regional SWH prediction. In order to explore a new SWH prediction method, this paper proposes a deep neural network model for regional SWH prediction based on the attention mechanism, namely CBA-Net. In this study, the wind and wave height of the ERA5 data set in the South China Sea from 2011 to 2018 were used as input features to train the model to evaluate the SWH prediction performance at 1 h, 12 h, and 24 h. The results show that the single use of a convolutional neural network cannot accurately predict SWH. After adding the Bi-LSTM layer and attention mechanism, the prediction of SWH is greatly improved. In the 1 h SWH prediction using CBA-Net, SARMSE, SAMAPE, SACC are 0.299, 0.136, 0.971 respectively. Compared with the CNN + Bi-LSTM method that does not use the attention mechanism, SARMSE and SAMAPE are reduced by 43.4% and 48.7%, respectively, while SACC is increased by 5%. In the 12 h SWH prediction, SARMSE, SAMAPE, and SACC of CBA-Net are 0.379, 0.177, 0.954 respectively. In the 24 h SWH prediction, SARMSE, SAMAPE, and SACC of CBA-Net are 0.500, 0.236, 0.912 respectively. Although with the increase of prediction time, the performance is slightly lower than that of 12 h, the prediction error is still maintained at a small level, which is still better than other methods.

/pdf/a-prediction-model-of-significant-wave-height-in-the-south-tsgg8guj.pdf

Peng Hao

Papers

An improved grey multivariable model for predicting industrial energy consumption in China

Study of the influence mechanism of China's electricity consumption based on multi-period ST-LMDI model

Analysis of the Influence Mechanism of CO2 Emissions and Verification of the Environmental Kuznets Curve in China

Exploring the influencing factors of urban residential electricity consumption in China

A Prediction Model of Significant Wave Height in the South China Sea Based on Attention Mechanism