Y
Yun Bai
Researcher at Chongqing Technology and Business University
Publications - 82
Citations - 2429
Yun Bai is an academic researcher from Chongqing Technology and Business University. The author has contributed to research in topics: Mean absolute percentage error & Deep learning. The author has an hindex of 23, co-authored 73 publications receiving 1534 citations. Previous affiliations of Yun Bai include Dongguan University of Technology & University of the Algarve.
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Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions
TL;DR: In this paper, a model W-BPNN using wavelet technique and back propagation neural network (BPNN) is developed and tested to forecast daily air pollutants (PM 10, SO 2, and NO 2 ) concentrations.
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Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models
TL;DR: The addressed method integrates the deep framework with multiscale and hybrid observations, and therefore being good at exploring sophisticated natures in the reservoir inflow forecasting.
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An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting.
TL;DR: The E-LSTM model inspired by ensemble learning, which constructs multiple LSTMs in different modes, obtained better forecasting performance than that using the single LSTM and feed forward neural network in terms of mean absolute percentage error.
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Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator
TL;DR: A new unbiased grey prediction model called UGM(1,1) is proposed and optimised and found to outperform other grey models and be of important reference value for use by the Chinese government to formulate energy policies.
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Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition
TL;DR: A random forest model coupled with ensemble empirical mode decomposition (EEMD) named EEMD-RF is presented for forecasting the daily electricity consumption of general enterprises and exhibited the best forecast performance in terms of mean absolute error, mean absolute percentage error, and root-mean-square error.