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Bo Zeng
Researcher at Chongqing Technology and Business University
Publications - 68
Citations - 3332
Bo Zeng is an academic researcher from Chongqing Technology and Business University. The author has contributed to research in topics: Mean absolute percentage error & Energy consumption. The author has an hindex of 27, co-authored 65 publications receiving 2068 citations. Previous affiliations of Bo Zeng include College of Business Administration & Dongguan University of Technology.
Papers
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A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China
TL;DR: A novel fractional grey model called the fractional time delayed grey model, which significantly outperforms the other 8 existing grey models is proposed and applied to forecast the coal and natural gas consumption of Chongqing China.
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Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model
TL;DR: A novel nonlinear grey Bernoulli model with fractional order accumulation, abbreviated as FANGBM(1,1) model, is proposed to forecast short-term renewable energy consumption of China during the 13th Five-Year Plan (2016–2020).
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Forecasting the natural gas demand in China using a self-adapting intelligent grey model
TL;DR: A self-adapting intelligent grey prediction model that can automatically optimize model parameters according to the real data characteristics of modeling sequence is proposed and used to forecast China's natural gas demand during 2015–2020.
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The conformable fractional grey system model.
TL;DR: The proposed conformable fractional grey model is more efficient in longer term prediction and non-smooth time series forecasting than the existing models and introduces the Brute Force method to optimize its fractional order.
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A novel grey forecasting model and its optimization
TL;DR: In this paper, a grey forecasting model and its optimized model were proposed for predicting the data sequence with the characteristics of non-homogeneous exponential law, and the results showed that the proposed model and it optimized model can enhance the prediction accuracy.