77 Papers
156 Citations
Yaoguo Dang is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Computer science & Interval (mathematics). The author has an hindex of 15, co-authored 63 publications. Previous affiliations of Yaoguo Dang include Nanjing University.
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Papers
Forecasting China's electricity consumption using a new grey prediction model
TL;DR: The two empirical results illustrate that the novel initial condition with dynamic weighted coefficients can better adjust to the features of electricity consumption data than the previous initial conditions and show the superiority of the newly proposed model over the benchmark models.
254
Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China
Ning Xu,Yaoguo Dang,Yande Gong +2 more
TL;DR: Wang et al. as discussed by the authors proposed an optimized hybrid GM(1,1) model to improve prediction accuracy of electricity energy consumption (EEC) in short-term time response function (TRF).
150
Forecasting Chinese CO 2 emissions from fuel combustion using a novel grey multivariable model
TL;DR: Wang et al. as mentioned in this paper proposed a grey multivariable model to predict future CO2 emissions from fuel combustion from 2014 to 2020, and the forecasted results can provide a solid basis for formulating environmental policies and energy consumption plans.
147
Optimal modeling and forecasting of the energy consumption and production in China
TL;DR: In this paper, a GM (gray model) (1,1) model based on optimizing initial condition according to the principle of new information priority was proposed to predict China's energy consumption and production from 2013 to 2017.
113
Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China
TL;DR: A self-adaptive grey fractional weighted model is established to predict Jiangsu’s electricity consumption, which efficiently enhances the prediction quality of electricity consumption.
93