Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

93
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.
93
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

621
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...
621
Distributed Loads01:19

Distributed Loads

508
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...
508
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.4K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.4K
Energy and Power Signals01:17

Energy and Power Signals

244
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
244
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Electric vehicle charging station recommendation system based on graph neural network and context-aware refinement.

Scientific reports·2026
Same author

Author Correction: Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting.

Scientific reports·2025
Same author

Data pipeline for real-time energy consumption data management and prediction.

Frontiers in big data·2024
Same author

Attention-based speech feature transfer between speakers.

Frontiers in artificial intelligence·2024
Same author

Hierarchical PtCuMnP Nanoalloy for Efficient Hydrogen Evolution and Methanol Oxidation.

Small methods·2024
Same author

Enhanced Thermal Stability and Conductivity of FeF<sub>3</sub> Using Ni-Coated Carbon Composites: Application as High-Temperature Cathodes in Thermal Batteries.

Nanomaterials (Basel, Switzerland)·2023

Related Experiment Video

Updated: May 30, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

Learning model combined with data clustering and dimensionality reduction for short-term electricity load

Hyun-Jung Bae1, Jong-Seong Park1, Ji-Hyeok Choi2

  • 1Graduate School of Data Science, Seoul National University of Science and Technology, Seoul, South Korea.

Scientific Reports
|January 28, 2025
PubMed
Summary

This study introduces a new short-term load forecasting model using data clustering and dimensionality reduction. The enhanced model significantly improves prediction accuracy for electricity usage, outperforming existing methods.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

Related Experiment Videos

Last Updated: May 30, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

Area of Science:

  • Electrical Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Electric load forecasting is vital for electric power companies' planning and operations.
  • Traditional statistical methods have advanced to artificial intelligence (AI)-based techniques using machine learning (ML).
  • Handling large-scale electricity usage datasets presents significant challenges for accurate forecasting.

Purpose of the Study:

  • To propose a novel prediction model for short-term load forecasting (STLF) tailored for large-scale electricity usage data.
  • To effectively manage and analyze extensive electricity consumption datasets through integrated data clustering and dimensionality reduction.
  • To enhance the performance of neural network-based STLF models.

Main Methods:

  • Adapted k-means clustering for data clustering.
  • Employed kernel principal component analysis (kernel PCA), universal manifold approximation and projection (UMAP), and t-stochastic nearest neighbor (t-SNE) for dimensionality reduction.
  • Validated the proposed model by applying it to neural network-based models using actual electricity usage data from 4710 households.

Main Results:

  • Experimental results confirm that combining data clustering with dimensionality reduction enhances baseline model performance.
  • The proposed method demonstrated superior prediction accuracy compared to existing methods.
  • Achieved performance improvements of 1.01-1.76 times for summer data and 1.03-1.36 times for winter data in terms of mean absolute percentage error (MAPE).

Conclusions:

  • Data clustering and dimensionality reduction are effective strategies for improving STLF accuracy on large datasets.
  • The proposed hybrid approach offers a significant advancement in STLF, particularly for large-scale electricity consumption.
  • The method provides a robust solution for electric power companies seeking to optimize planning and operations through accurate load forecasting.