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8 results on '"Wuxiao Chen"'

1. Electricity-carbon modeling of flat glass industry based on correlation variable

Author

Guoshu Lai, Qiang Ye, Wuxiao Chen, Zeyan Hu, Liang Hong, Yu Wang, and Yuqing Cai

Subjects
Flat glass industry, Electricity-carbon model, Carbon emission prediction, Support Vector Regression (SVR), Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The flat glass industry is a typical industry with high energy consumption and extensive carbon emission. The carbon emission of flat glass industry in China ranks first in the same industry in the world. At present, there are few researches on carbon emission prediction for industrial enterprises, especially for the flat glass industry, due to lack of monitoring data. This paper selects electricity consumption as the influencing factor of carbon emission. This paper firstly preprocesses the electricity consumption data according to the China greenhouse gas emission standard. Next, this paper selects a correlation variable to fit the historical data of the flat glass industry based on Support Vector Regression (SVR). Finally, it obtains the parameters of the basic form of the electricity consumption to carbon emission model of the industry. The validity of the electricity-carbon modeling method and the accuracy of the model are verified by simulation experiments. The electricity-carbon model established by the method in this paper has a high accuracy rate, and the coefficient of determination (R2) reaches 0.98, which can play an auxiliary role in the verification of carbon emissions.

Published
2022
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6. The Application Research of Deep Learning Based Method for Short-term Wind Speed Forecasting

Author

Wuxiao Chen, Bin Chen, Renshu Wang, Huoduan Lin, and Bingqian Liu

Subjects
business.industry, Computer science, Deep learning, Context (language use), Variation (game tree), Machine learning, computer.software_genre, Convolutional neural network, Wind speed, Term (time), Artificial intelligence, Time series, business, computer, Randomness
Abstract

Short-term wind speed forecasting is a difficult work for the randomness of wind speed variation. With the advantage of deep learning, a novel model is proposed to improve the performance of short-term wind speed forecasting. Different from the normal deep learning model, the framework of this new model is composed of convolutional neural network (CNN) and long short term memory (LSTM), extracting common features on high-dimensional time series data at different levels with a more stable prediction effect by CNN and utilizing the context information on the time axis based on the excellent memory ability of the time series data of LSTM. Meanwhile, for the purpose of accelerating calculation and mining the features of data better, the inception structure is also introduced. At last, the experiment results are presented and it shows the improvement of the proposed method, compared to the basic LSTM model.

Published
2019
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