Search Results for author: Chongqing Kang

Found 7 papers, 2 papers with code

Improving Sample Efficiency of Deep Learning Models in Electricity Market

no code implementations11 Oct 2022 Guangchun Ruan, Jianxiao Wang, Haiwang Zhong, Qing Xia, Chongqing Kang

The superior performance of deep learning relies heavily on a large collection of sample data, but the data insufficiency problem turns out to be relatively common in global electricity markets.

Data Augmentation

Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

1 code implementation10 Dec 2021 Guangchun Ruan, Zekuan Yu, Shutong Pu, Songtao Zhou, Haiwang Zhong, Le Xie, Qing Xia, Chongqing Kang

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation.

Estimating Demand Flexibility Using Siamese LSTM Neural Networks

no code implementations3 Sep 2021 Guangchun Ruan, Daniel S. Kirschen, Haiwang Zhong, Qing Xia, Chongqing Kang

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices.

Tracking and Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector

1 code implementation11 May 2020 Guangchun Ruan, Dongqi Wu, Xiangtian Zheng, S. Sivaranjani, Le Xie, Haiwang Zhong, Chongqing Kang

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U. S. becoming the epicenter of COVID-19 cases and deaths in late March.

Computers and Society

Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding

no code implementations20 Apr 2020 Qingchun Hou, Ning Zhang, Daniel S. Kirschen, Ershun Du, Yaohua Cheng, Chongqing Kang

Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure.

Bounding Regression Errors in Data-driven Power Grid Steady-state Models

no code implementations30 Oct 2019 Yuxiao Liu, Bolun Xu, Audun Botterud, Ning Zhang, Chongqing Kang

Results identify how the bounds decrease with additional power grid physical knowledge or more training data.

regression

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