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相关概念视频

Distributed Loads01:19

Distributed Loads

927
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...
927
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
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...
1.1K
Energy and Power Signals01:17

Energy and Power Signals

1.0K
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:
1.0K
Transient and Steady-state Response01:24

Transient and Steady-state Response

500
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
500
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

576
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.
576
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

714
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:
714

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相关实验视频

Updated: Jun 27, 2026

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

轻量级机器学习框架使用时间特征用于边缘设备上的电动汽车需求响应预测.

Ali Mujtaba Durrani1, Azzam Ul Asar1, Abdul Aziz2

  • 1Department of Electrical Engineering, CECOS University of IT and Emerging Sciences, Peshawar, KPK, Pakistan.

Scientific reports
|December 8, 2025
PubMed
概括

轻量级机器学习模型有效地预测电动汽车 (EV) 充电负载,并优化需求响应 (DR) 策略,即使在低计算环境中. 在电动汽车能源管理方面,XGBoost和Random Forest显示了最高的精度.

关键词:
需求响应是对需求的反应.电动汽车充电充电电动汽车充电能源效率 能源效率是指能源的使用效率.预测 预测 预测 预测负载转移的转移机器学习 机器学习在XgBoost中使用XgBoost.

相关实验视频

Last Updated: Jun 27, 2026

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

科学领域:

  • 能源管理 能源管理
  • 机器学习 机器学习
  • 电动车辆 电动车辆

背景情况:

  • 电动汽车 (EV) 普及的增加给电网能源管理带来了重大挑战,特别是在资源有限的环境中.
  • 有效的需求响应 (DR) 策略对于平衡能源供应和需求以及增加电动汽车集成至关重要.

研究的目的:

  • 开发和评估轻量级机器学习 (ML) 模型,以准确预测电动汽车充电负载.
  • 使用预测EV负载配置文件优化各种需求响应 (DR) 策略.
  • 对低资源环境的预测准确性和计算效率来评估模型性能.

主要方法:

  • 利用Kaggle数据集的时间序列电动汽车充电数据,进行预处理,下采样和特征工程.
  • 实施并比较了五种ML模型:线性回归 (LR),支向量回归 (SVR),k-最近邻居 (kNN),随机森林 (RF) 和极端梯度增强 (XGBoost).
  • 通过MAE,RMSE和R2指标评估了七个DR策略 (峰值剪切,山谷填充,负载转移,负载平衡,战略负载增长,战略保护,灵活的负载形状).

主要成果:

  • XGBoost表现出最高的准确性,在战略性保护方面获得了0.975的R2分,在山谷填充方面获得了0.943分.
  • 随机森林也表现良好,R2得分为0.91,表明强大的预测能力.
  • 线性回归和kNN模型的性能明显较低,R2值在大多数DR策略中很少超过0.50.

结论:

  • 轻量级的ML模型能够为EV负载预测和DR建模提供高性能.
  • 这些模型为电网运营商和政策制定者提供了可扩展的解决方案,用于具有有限计算资源的环境.
  • 这些发现突显了优化 DR 策略的潜力,这些策略由高效的 ML 提供动力,用于管理电动汽车的能源需求.