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151 results on '"Bai, Mingliang"'

1. HouYi: An open-source large language model specially designed for renewable energy and carbon neutrality field

Author

Bai, Mingliang, Zhou, Zhihao, Wang, Ruidong, Yang, Yusheng, Qin, Zizhen, Chen, Yunxiao, Mu, Chunjin, Liu, Jinfu, and Yu, Daren

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Renewable energy is important for achieving carbon neutrality goal. With the great success of Large Language Models (LLMs) like ChatGPT in automatic content generation, LLMs are playing an increasingly important role. However, there has not been a specially designed LLM for renewable energy. Meanwhile, there has not been any dataset of renewable energy for training LLMs. Therefore, this paper published the first open-source Renewable Energy Academic Paper (REAP) dataset for non-commercial LLM research of renewable energy. REAP dataset is collected through searching the title and abstract of 1,168,970 academic literatures from Web of Science. Based on REAP dataset, HouYi model, the first LLM for renewable energy, is developed through finetuning general LLMs. HouYi demonstrated powerful academic paper paragraph generation ability in renewable energy field. Experiments show that its ability to generate academic papers on renewable energy is comparable to ChatGPT, slightly outperforms Claude, ERNIE Bot and SparkDesk, and significantly outperforms open-source LLaMA-13B model.

Published
2023

7. Short-term probabilistic photovoltaic power forecast based on deep convolutional long short-term memory network and kernel density estimation

Author

Bai, Mingliang, Zhao, Xinyu, Long, Zhenhua, Liu, Jinfu, and Yu, Daren

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Solar energy is a clean and renewable energy. Photovoltaic (PV) power is an important way to utilize solar energy. Accurate PV power forecast is crucial to the large-scale application of PV power and the stability of electricity grid. This paper proposes a novel method for short-term photovoltaic power forecast using deep convolutional long short-term memory (ConvLSTM) network and kernel density estimation (KDE). In the proposed method, ConvLSTM is used to forecast the future photovoltaic power and KDE is used for estimating the joint probabilistic density function and giving the probabilistic confidence interval. Experiments in an actual photovoltaic power station verify the effectiveness of the proposed method. Comparison experiments with convolutional neural network (CNN) and long short-term memory network (LSTM)shows that ConvLSTM can combine the advantages of both CNN and LSTM and significantly outperform CNN and LSTM in terms of forecast accuracy. Through further comparison with other five conventional methods including multilayer perceptron (MLP), support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART) and gradient boosting decision tree (GBDT), ConvLSTM can significantly improve the forecast accuracy by more than 20% for most of the five methods and the superiorities of ConvLSTM are further verified.

Published
2021

36. Research on Dynamic Resistance and Magnetization Loss of CORC Windings for Superconducting AC Homopolar Machines

Author

Yan, Juzhuang, Yang, Wenjiang, Bai, Mingliang, Zhao, Peng, Hu, Juntong, and Zheng, Jinxing

Abstract

In the application of the superconducting ac homopolar machines (SAHMs), high temperature superconductor (HTS) exciting windings carry dc currents under external ac magnetic fields and generally cause dynamic loss and magnetization loss, which will deteriorate the stable operation of SAHM. The conductor on round core (CORC) cables is a promising choice for HTS exciting windings because of its high current-carrying capacity with low ac losses and excellent mechanical stability. In this study, the dynamic resistance and magnetization losses of CORC cables carrying a dc current under an ac magnetic field were studied using numerical and experimental methods at 77 K. The numerical methods [three-dimensional (3-D) H homogenization model, 3-D T-A full model, 3-D T-A homogenization model] were compared with experimental data to validate them. Subsequently, the current distribution of CORC cables was studied under different working conditions. The effect of weak anisotropy and the shielding effect of CORC cables on dynamic resistance was researched. In addition, the dependence of CORC structure parameters on dynamic resistance and magnetization losses was analyzed, which is very helpful for the engineering applications of CORC cables as the exciting windings in homopolar machines. Besides, the results provide a reference for dynamic loss behaviors in CORC superconductor.

Published
2024
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41. A comparative study on class-imbalanced gas turbine fault diagnosis.

Author

Bai, Mingliang, Liu, Jinfu, Long, Zhenhua, Luo, Jing, and Yu, Daren

Subjects
FAULT diagnosis, GAS turbines, ARTIFICIAL neural networks, MACHINE learning, COMPARATIVE studies, CONCEPT learning
Abstract

Gas turbines are widely used in various fields, and the failure of gas turbines can cause catastrophic consequences. Health condition monitoring and fault diagnosis of gas turbines can detect faults timely, avoid serious faults, and significantly reduce maintenance costs. Thus, fault diagnosis of gas turbines has great significance. Current researches on gas turbine fault diagnosis mainly focus on the case of abundant fault samples. However, fault data are very rare and the number of normal samples is much larger than the number of fault samples in the industrial scene. This class-imbalance problem widely exists but is hardly focused in the field of gas turbine fault diagnosis. Aiming to solve this problem, this paper introduces the concept of class-imbalanced learning from the machine learning field, summarizes three kinds of class-imbalance addressment methods including oversampling, undersampling, and sample weighting, and proposes a new combination method of focal loss and random oversampling for addressing class-imbalance in deep neural networks, and performs a systematic comparative study on class-imbalanced gas turbine fault diagnosis. Experimental results show that class-imbalance can seriously reduce the fault diagnosis accuracy. Through these class-imbalance addressment methods, diagnosis accuracy is greatly improved. Comparative experiments also show that the proposed combination method can obtain the best diagnosis accuracy among all the compared methods in class-imbalanced situation. Through this comparative study, a detailed guideline for improving diagnosis accuracy under class-imbalanced circumstance is provided. [ABSTRACT FROM AUTHOR]

Published
2023
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