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21 results on '"Li, Wenfang"'

5. Data-driven User Behavior Prediction Based on Integrated Prediction Model

Author

Jun Fang, Jingfan Zou, Bing Ling, Li Wenfang, Weigang Xu, and Xiangyu Zhang

Subjects
Price elasticity of demand, Flexibility (engineering), Demand response, Incentive, Elasticity (cloud computing), Order (exchange), Computer science, Data mining, Grid, computer.software_genre, computer, Data-driven
Abstract

Multi-station integration of various substations to participate in grid dispatching can fully mobilize load-side resources to participate in the power market, improve grid flexibility, and reduce grid peaking pressure, but the uncertainty of user behavior limits the development of demand response services. In response to this problem, this article first builds an incentive-based demand response implementation framework, expounds how load service entities gather demand-side resources to participate in the power market business, and convert the behavior of users responding to incentive policies to demand elasticity. Then, considering that the prediction model will ultimately need to be applied online, this paper proposes a data-driven incentive demand response user behavior prediction method that integrates LSTM based on the long short memory (LSTM) algorithm. At the same time, in order to improve the performance of the prediction model, The original data is smoothed and scaled, and the weight coefficient of the loss function is added. The results of the calculation examples show that, compared with the traditional LSTM algorithm and the k-neighbor prediction method, the prediction method proposed in this paper can significantly improve the prediction accuracy, and the preprocessing of the original data has positive significance for improving the prediction accuracy.

Published
2021

6. Small-scale Source-network-load-storage Friendly Interaction Fusion Method

Author

Bing Ling, Jun Fang, Li Wenfang, Weigang Xu, Jingfan Zou, and Xiangyu Zhang

Subjects
Scale (ratio), Computer science, Control system, Peaking power plant, Photovoltaic system, Economic evaluation, Weather forecasting, computer.software_genre, computer, Energy storage, Energy (signal processing), Reliability engineering
Abstract

This paper proposes a practical method for formulating energy storage-assisted conventional unit peak shaving schemes based on multi-station integration of comprehensive technical evaluation indicators and economic evaluation indicators for switching stations, and adopts a planning system for regional power grids and different new energy generation penetration scenarios Two calculation examples were used for simulation and analysis, and energy storage auxiliary peak shaving schemes for conventional units were formulated for different scenarios. Finally, the feasibility of the method is verified through experiments.

Published
2021

13. Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks.

Author

Chen, Jiangang, He, Chao, Yin, Jintao, Li, Jiawei, Duan, Xiaoqian, Cao, Yucheng, Sun, Li, Hu, Menghan, Li, Wenfang, and Li, Qingli

Subjects
COVID-19, ULTRASONIC imaging, LUNGS, MEDICAL personnel, QUANTITATIVE research, SUPPORT vector machines
Abstract

As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with $128\times256$ two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics. [ABSTRACT FROM AUTHOR]

Published
2021
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