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

Energy and Power Signals01:17

Energy and Power Signals

1.2K
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:
1.2K
Survival Tree01:19

Survival Tree

451
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
451
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

653
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.
653
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

15
Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression...
15
Load-frequency control01:28

Load-frequency control

700
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
700
The Entropy as a State Function01:14

The Entropy as a State Function

11
Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
11

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

Dual-Region Encryption Model Based on a 3D-MNFC Chaotic System and Logistic Map.

Entropy (Basel, Switzerland)·2026
Same author

Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy.

Entropy (Basel, Switzerland)·2024
Same author

Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.

Entropy (Basel, Switzerland)·2024
Same author

Continuous Dictionary of Nodes Model and Bilinear-Diffusion Representation Learning for Brain Disease Analysis.

Brain sciences·2024
Same author

Impaired Brain Information Transmission Efficiency and Flexibility in Parkinson's Disease and Rapid Eye Movement Sleep Behavior Disorder: Evidence from Functional Connectivity and Functional Dynamics.

Parkinson's disease·2022
Same author

Systemic analyses of immunophenotypes of peripheral T cells in non-segmental vitiligo: implication of defective natural killer T cells.

Pigment cell & melanoma research·2012
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

组合与自适应深度融合用于短期电力负载预测.

Yiling Wang1, Yan Niu1, Xuejun Li2

  • 1College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的集成与自适应深度融合 (EEADF) 框架,用于准确的短期功率负载预测. 通过有效地融合多个特征信息并捕捉复杂的系统动态,EEADF方法提高了预测准确性.

关键词:
这是LSTM的LSTM.适应性融合适应性融合深度学习是一种深度学习.整体的.多模式时间序列分析.预测功率负载的预测

相关实验视频

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:

  • 电力负载预测对于稳定和经济的电力系统运行至关重要.
  • 传统的方法难以应对功率负载数据的复杂性和非静止性.
  • 挑战包括捕捉瞬间动态和有效地融合多功能信息.

研究的目的:

  • 为短期多特征功率负载预测提出一个新的框架,即带有自适应深度融合 (EEADF) 的集体透.
  • 通过解决现有方法的局限性来提高功率负载预测的准确性和稳定性.

主要方法:

  • 开发了一组即时提取模块,用于计算和融合近似,样本和顺序.
  • 实施了适应任务的层次融合机制,包括特征连接和多头自我注意力融合.
  • 使用双分支深度学习模型并行处理原始序列 (LSTM) 和特征 (MLP).

主要成果:

  • 欧洲农业开发基金框架在模拟数据上识别各种动态模式方面表现出强大 (MSE:0.0125,MAE:0.0794,R2:0.9932).
  • 在现实世界ETDataset上,EEADF显著超过了基线模型 (LSTM,TCN,变压器,Informer) 和传统方法.
  • 废弃研究证实了特征和融合机制对预测准确性的重要性.

结论:

  • 拟议的欧洲开发基金框架在短期多特征功率负载预测方面取得了重大进展.
  • 该方法有效地捕捉了系统动态,并融合了多模式信息,从而实现了卓越的预测性能.
  • 欧洲发电开发基金为实际的电力系统管理提供了强大而准确的解决方案.