Jove
Visualize
联系我们

相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
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
2.8K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.1K
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...
1.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.1K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.1K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

725
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
725

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

Intelligent Soft Sensor for Spindle Convective Heat Transfer Coefficient Under Varying Operating Conditions Using Improved Grey Wolf Optimization Algorithm.

Sensors (Basel, Switzerland)·2025
查看所有相关文章
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

相关实验视频

Updated: Jan 15, 2026

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

11.0K

一个高精度的短期光伏功率预测模型,基于多变量变量模式分解和门循环单元注意力,带有Crested Porcupine优化器增强的矢量加权平均算法.

Jinxiang Pian1, Xianliang Chen1

  • 1School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
概括

一个新的混合型号提高了光伏 (PV) 短期功率预测的准确性. 这种先进的系统将数据分解与优化的神经网络相结合,增强可再生能源的整合和电网稳定性.

关键词:
预测光伏功率的预测注意力机制注意力机制有门的循环单元.多变量变量模态分解矢量加权平均算法 矢量加权平均算法

相关实验视频

Last Updated: Jan 15, 2026

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

11.0K

科学领域:

  • 可再生能源系统可再生能源系统
  • 人工智能在能源中的作用
  • 电力系统工程 电力系统工程

背景情况:

  • 对光伏 (PV) 系统等可再生能源的日益依赖对于可持续发展至关重要.
  • 准确的短期光伏电力预测对于高效的电网整合至关重要,但仍然具有挑战性.
  • 现有的预测方法经常与光伏发电的波动性和复杂性作斗争.

研究的目的:

  • 开发一种新的混合预测模型,以提高短期光伏发电预测的准确性.
  • 通过先进的预测技术,提高分布式光伏系统的效率和可靠性.
  • 解决当前预测模型在捕捉复杂的光伏功率动态方面的局限性.

主要方法:

  • 一个混合模型,集成多变量变化模式分解 (MVMD) 与门式反复单位 (GRU) 网络,注意力机制 (ATT) 和增强的矢量加权平均算法 (cINFO).
  • 使用MVMD进行数据分解以减少波动.
  • 使用Crested Porcupine Optimizer (CPO) 的INFO算法的优化版本cINFO算法被用于微调GRU-ATT的超参数,ATT专注于关键影响因素.

主要成果:

  • 拟议的模型在阳光条件下在DKASC爱丽斯斯普林斯数据集上实现了高预测准确度.
  • 关键性能指标包括0.0249的平均绝对误差 (MAE),0.0693的根平均平方误差 (RMSE) 和99.79%的确定系数 (R2).
  • 该模型显著优于基准模型,证明了其卓越的预测能力.

结论:

  • 开发的混合 MVMD-GRU-ATT-cINFO 模型对于短期光伏电力预测是可行的和优越的.
  • 这些发现支持该模型在增强光伏系统融入电网方面的潜力.
  • 这项研究为克服可再生能源预测准确性的局限性提供了强有力的解决方案.