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相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

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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).
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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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.
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Energy and Power Signals01:17

Energy and Power Signals

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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:
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Calculation of Electric Flux01:25

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Consider the electric field of an oppositely charged, parallel-plate system and an imaginary box between those plates. Let the bottom face of the box be ABCD, and the top face be FGHK. The electric field between the plates is uniform and points from the positive plate toward the negative plate. The calculation of this field's flux through the box's various faces shows that the net flux through the box is zero. Why does the flux cancel out here?
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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.
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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贝叶斯的方法用于建模和预测太阳能光伏发电.

Mariana Villela Flesch1, Carlos Alberto de Bragança Pereira2, Erlandson Ferreira Saraiva3

  • 1Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil.

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概括

这项研究引入了使用高斯过程的贝叶斯方法,以准确地建模和预测每日太阳能发电曲线. 该方法提供了流的函数估计,并表现出优异的性能与低误差率.

关键词:
贝叶斯的推理 贝叶斯的推理斯过程是高斯过程.吉布斯采样算法 吉布斯采样算法美国MCMCMCMCMCMCMCMC光伏太阳能发电预测的预测统计建模 统计建模

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科学领域:

  • 统计 统计 统计 统计
  • 可再生能源的建模.
  • 机器学习 机器学习

背景情况:

  • 准确的太阳能预报对于电网管理至关重要.
  • 传统的方法可能会与太阳能发电的固有变化和复杂模式作斗争.
  • 贝叶斯式方法为时间序列建模中的不确定性量化提供了一个强大的框架.

研究的目的:

  • 开发一个贝叶斯的方法来估计和预测每日太阳能发电曲线.
  • 为了模拟太阳能发电的未知功能,使用高斯过程.
  • 通过插值提供流的函数估计,以改善预测.

主要方法:

  • 使用贝叶斯模型与高斯过程在每日太阳能价值之前.
  • 采用吉布斯采样算法来估计模型参数,因为缺乏已知的后部分布形式.
  • 通过从k变量正常分布中插入点来估计平滑函数.

主要成果:

  • 建议的贝叶斯式方法有效地建模和预测太阳能发电曲线.
  • 模拟研究和现实世界的数据应用显示了接近零的平均绝对百分比误差 (MAPE) 和根-平均-平方误差 (RMSE).
  • 该方法产生了流的函数估计,表明高精度和可靠性.

结论:

  • 贝叶斯方法与高斯过程是太阳能电力曲线估计和预测的高效工具.
  • 吉布斯采样实现为复杂模型提供了准确的参数估计.
  • 该方法显示了改善太阳能管理和集成到电网中的巨大潜力.