Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
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

相关实验视频

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

学习模型与数据聚类和维度缩小相结合,用于短期电力负载预测.

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
概括

本研究引入了一种新的短期负载预测模型,使用数据聚类和维度减少. 改进后的模型显著提高了对电力使用的预测准确度,性能优于现有的方法.

更多相关视频

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

相关实验视频

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

科学领域:

  • 电气工程 电气工程
  • 数据科学数据科学数据科学
  • 人工智能的人工智能

背景情况:

  • 电力负载预测对于电力公司的规划和运营至关重要.
  • 传统的统计方法已经发展到基于人工智能 (AI) 的技术,使用机器学习 (ML).
  • 处理大规模的电力使用数据集对准确的预测提出了重大挑战.

研究的目的:

  • 为短期负载预测 (STLF) 提出一种针对大规模电力使用数据量身定制的新型预测模型.
  • 通过集成数据聚类和维度减少,有效管理和分析大量的电力消耗数据集.
  • 为了提高基于神经网络的STLF模型的性能.

主要方法:

  • 适应的k-means集群用于数据集群.
  • 采用了核心主要组件分析 (PCA),通用多元体近似和投影 (UMAP) 和t-静态近邻 (t-SNE) 进行维度缩小.
  • 通过将其应用于基于神经网络的模型,使用来自4710个家庭的实际电力使用数据来验证拟议的模型.

主要成果:

  • 实验结果证实,将数据聚类与维度减少相结合,可以提高基线模型的性能.
  • 与现有方法相比,拟议的方法显示出更高的预测准确性.
  • 在平均绝对百分比误差 (MAPE) 方面,夏季数据的1.01-1.76倍和冬季数据的1.03-1.36倍的性能改进.

结论:

  • 数据聚类和维度减小是提高STLF在大型数据集上的准确性的有效策略.
  • 拟议的混合方法为STLF提供了显著的进步,特别是在大规模的电力消耗方面.
  • 该方法为寻求通过准确的负载预测优化规划和运营的电力公司提供了强大的解决方案.