An edge-AI based forecasting approach for improving smart microgrid efficiency

L Lv, Z Wu, L Zhang, BB Gupta… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Smart Grid 2.0 is the energy Internet based on advanced metering infrastructure and
distributed systems that require an instantaneous two-way flow of energy information. Edge …

Efficient residential electric load forecasting via transfer learning and graph neural networks

D Wu, W Lin - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The accurate short-term electric load forecasting (STLF) is critical for the safety and
economical operation of modern electric power systems. Recently, the graph neural network …

Gradient-based neural networks for solving periodic Sylvester matrix equations

L Lv, J Chen, L Zhang, F Zhang - Journal of the Franklin Institute, 2022 - Elsevier
This paper considers neural network solutions of a category of matrix equation called
periodic Sylvester matrix equation (PSME), which appear in the process of periodic system …

Mining and application of tourism online review text based on natural language processing and text classification technology

H Xu, Y Lv - Wireless Communications and Mobile Computing, 2022 - Wiley Online Library
This paper firstly describes the research status of online review text mining and finds out the
problems existing in the mining and application of tourism texts. Aiming at these problems …

[HTML][HTML] Short-term wind power forecasting and uncertainty analysis based on FCM–WOA–ELM–GMM

B Gu, H Hu, J Zhao, H Zhang, X Liu - Energy Reports, 2023 - Elsevier
With large-scale wind power connected to the power grid, accurate short-term wind power
forecasting has become a key technology for safe, economic power grid operation …

Pseudo-correlation problem and its solution for the transfer forecasting of short-term natural gas loads

N Wei, L Yin, C Yin, J Liu, S Wang, W Qiao… - Gas Science and …, 2023 - Elsevier
Considering the information protection of users and system failure of natural gas enterprises,
complete large-scale data are difficult to obtain, which is a critical issue for data-driven …

ResNet-integrated very early bolt looseness monitoring based on intrinsic feature extraction of percussion sounds

R Yuan, Y Lv, S Xu, L Li, Q Kong… - Smart Materials and …, 2023 - iopscience.iop.org
Very early bolt looseness monitoring has been a challenge in the field of structural health
monitoring. The authors have conducted a further study of the previous researches, with the …

Short-term wind speed and power forecasting for smart city power grid with a hybrid machine learning framework

Z Wang, L Wang, M Revanesh… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
To address foreseeable challenges during the penetration of wind energy into the power
grid, including accurate wind power forecasting and smart power generation scheduling, this …

Construction and application of short-term and mid-term power system load forecasting model based on hybrid deep learning

H Xu, G Fan, G Kuang, Y Song - Ieee Access, 2023 - ieeexplore.ieee.org
Power system load forecasting model plays an important role in all aspects of power system
planning, operation and control. Therefore, accurate power load forecasting provides an …

A novel hybrid approach to mooring tension prediction for semi-submersible offshore platforms

L Yuan, Y Chen, Y Zan, S Zhong, M Jiang, Y Sun - Ocean Engineering, 2023 - Elsevier
The accuracy of mooring tension prediction significantly affects the safety of semi-
submersible offshore platform operations and the efficiency of production planning …