Results 211 to 220 of about 8,152,507 (265)

Short-Term Forecasting Based on Graph Convolution Networks and Multiresolution Convolution Neural Networks for Wind Power

IEEE Transactions on Industrial Informatics, 2023
Accurate prediction of wind power generation is of great significance for the efficient operation of wind farms. However, traditional deep learning-based methods predict the wind power without simultaneously considering the temporal features of wind ...
Yue Song, D. Tang, Jinsong Yu, Zetian Yu, X. Li   +4 more
semanticscholar   +1 more source

Optimal Graph Structure Based Short-Term Solar PV Power Forecasting Method Considering Surrounding Spatio-Temporal Correlations

IEEE transactions on industry applications, 2023
Accurate short-term photovoltaic (PV) power forecasting is of great significance for the safe and stable operation of power system. Spatial information from neighboring PV sites contributes to improving forecasting performance.
Meng Zhang, Z. Zhen, Nian Liu, Hongjun Zhao, Yiqian Sun, C. Feng, Fei Wang   +6 more
semanticscholar   +1 more source

A Spatiotemporal Directed Graph Convolution Network for Ultra-Short-Term Wind Power Prediction

IEEE Transactions on Sustainable Energy, 2023
The expansion of wind generation and the advance in deep learning have provided feasibility for multisite wind power prediction motivated by spatiotemporal dependencies.
Zhuo Li, Lin Ye, Yongning Zhao, Ming Pei, Peng Lu, Yilin Li, Binhua Dai   +6 more
semanticscholar   +1 more source

Learning Power Allocation for Multi-Cell-Multi-User Systems With Heterogeneous Graph Neural Networks

IEEE Transactions on Wireless Communications, 2022
A well-trained deep neural network (DNN) enables real-time resource allocation by learning the relationship between a policy and its impacting parameters.
Jia Guo, Chenyang Yang
semanticscholar   +1 more source

Directed Power Graphs

2021
In this article, first we introduce six types of power graphs related to a graph (or directed graph), with the help of set theory.Then we show that these newly defined power graphs are pairwise distinct by a few examples. Finally, we discuss the relation between Eulerian being the base graph and these six power graph types.
Mokhtarian Dehkordi, Elham, Eshaghi, Madjid, Moradi, Sirous, Yarahmadi, Zahra   +3 more
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Powers of graphs

Proceedings of the sixteenth annual ACM symposium on Theory of computing - STOC '84, 1984
In this paper we investigate a powerful, and yet simple, technique for devising approximation algorithms for a wide variety of NP-complete problems in routing, location, and communication network design. Each of the algorithms presented here delivers an approximate solution guaranteed to be within a constant factor of the optimal solution. In addition,
Dorit S. Hochbaum, David B. Shmoys
openaire   +1 more source

Parameter Identification in Power Transmission Systems Based on Graph Convolution Network

IEEE Transactions on Power Delivery, 2022
Parameter Identification plays an important role in electric power transmission systems. Existing approaches for parameter identification tasks typically have two limitations: (1) They generally ignored development trend of historical data, and did not ...
Zhiwei Wang, Min Xia, Min Lu, Lingling Pan, Jun Liu   +4 more
semanticscholar   +1 more source

Graph-Theory-Based Derivation, Modeling, and Control of Power Converter Systems

IEEE Journal of Emerging and Selected Topics in Power Electronics, 2022
Graph-theoretical approaches have been widely applied in many disciplines, however, their implementation in power electronics converters and systems is still in the exploring stage.
Yuzhuo Li, Johannes Kuprat, Y. Li, M. Liserre   +3 more
semanticscholar   +1 more source

Multi-Meteorological-Factor-Based Graph Modeling for Photovoltaic Power Forecasting

IEEE Transactions on Sustainable Energy, 2021
Solar energy is a strongly intermittent renewable energy source, which is affected by varied meteorological conditions, and thus produces arbitrary power outputs in photovoltaic (PV) power generation.
Lilin Cheng, Haixiang Zang, Tao Ding, Zhi-nong Wei, Guo-qiang Sun   +4 more
semanticscholar   +1 more source

A Physics-Guided Graph Convolution Neural Network for Optimal Power Flow

IEEE Transactions on Power Systems
The data-driven method with strong approximation capabilities and high computational efficiency provides a promising tool for optimal power flow (OPF) calculation with stochastic renewable energy.
Maosheng Gao, Juan Yu, Zhifang Yang, Junbo Zhao   +3 more
semanticscholar   +1 more source