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Related Concept Videos

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Profile Leveling and Cross Sections01:26

Profile Leveling and Cross Sections

Profile leveling and cross-sections are surveying methods used to determine and document terrain elevations for infrastructure projects such as highways, railroads, canals, and pipelines. These methods provide data for earthwork planning and alignment of proposed routes.  Profile leveling involves measuring elevations along a fixed line to create a vertical terrain profile. A surveyor sets up a leveling instrument at the benchmark (BM) and records a backsight (BS) to determine the instrument's...

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Related Experiment Video

Updated: Jun 11, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

DynEC: dynamic evolutionary clustering for power user load profiling using multi-view graph neural networks.

Lei Zhao1, Hong Zhao1, Mengjie Li1

  • 1State Grid Sichuan Electric Power Corporation, Chengdu, China.

Frontiers in Artificial Intelligence
|June 10, 2026
PubMed
Summary

Dynamic Evolutionary Clustering (DynEC) improves power user load profiling by modeling user behavior as a continuous process. This approach enhances accuracy and stability in dynamic power grids.

Keywords:
concept driftdynamic evolutionary clusteringgraph neural networksload profilingmulti-view learningsource-grid-load-storage

Related Experiment Videos

Last Updated: Jun 11, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Electrical Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Modern power grids feature dynamic and stochastic user behavior due to integrated systems.
  • Traditional static clustering models struggle with non-stationary user patterns and noise, leading to inaccuracies.

Purpose of the Study:

  • To introduce Dynamic Evolutionary Clustering (DynEC), a novel framework for robust power user load profiling.
  • To address the limitations of static models in capturing evolving user electricity consumption patterns.

Main Methods:

  • DynEC utilizes a multi-view graph neural network framework.
  • It constructs a dynamic graph incorporating geometric, temporal (dynamic time warping), and statistical features.
  • A gated spatiotemporal graph neural network optimizes latent representations balancing current data and historical trends.

Main Results:

  • DynEC demonstrated superior performance compared to existing baseline methods on real-world datasets.
  • The framework accurately identifies genuine concept drift in user behavior.
  • DynEC significantly reduces erroneous cluster switching, improving profiling stability.

Conclusions:

  • DynEC offers a stable and reliable solution for power user load profiling in evolving smart grids.
  • Modeling load profiling as a continuous evolutionary process overcomes static clustering limitations.
  • This approach supports more effective modern power grid management.