Investigation of Artificial Intelligence Vulnerability in Smart Grids: A Case from Solar Energy Forecasting-Reference-Cited by-同舟云学术

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Investigation of Artificial Intelligence Vulnerability in Smart Grids: A Case from Solar Energy Forecasting

Published:2023-12-15 Issue: Volume:39 Page:5207-5212
ISSN:
Container-title:2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2)
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Author:

Wang Qihan¹,Ruan Jiaqi²,Meng Xiangrui¹,Zhu Yifan¹,Liang Gaoqi³,Zhao Junhua¹

Affiliation:

1. School of Science and Engineering, The Chinese University of Hong Kong,Shenzhen, Shenzhen,China

2. The Hong Kong Polytechnic University,Department of Electrical and Electronic Engineering,Hong Kong,China

3. School of Mechanical Engineering and Automation, Harbin Institute of Technology,Shenzhen, Shenzhen,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10511272/10512350/10512845.pdf?arnumber=10512845

Reference25 articles.

1. A Long Short-Term Memory Based Deep Learning Method for Industrial Load Forecasting;Jiajia;Electric Power Construction,2018

2. A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting

3. Ultra-Short-Term Spatiotemporal Forecasting of Renewable Resources: An Attention Temporal Convolutional Network-Based Approach

4. Smart Grid Technologies: Communication Technologies and Standards

5. Adversarial Attacks on Deep Neural Networks for Time Series Classification

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