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相关概念视频

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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Distributed Loads: Problem Solving01:21

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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...
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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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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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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.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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基于Fennec Fox优化算法和混合内核极端机器学习的综合能源系统的多能负载预测方法.

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  • 1College of Science, Wuhan University of Science and Technology, Wuhan 430081, China.

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概括

集成能源系统 (IES) 的精确多能源负载预测是通过一种新的方法实现的,该方法结合了fennec fox优化算法 (FFA) 和混合内核极端学习机器,以提高能源可持续性.

关键词:
综合权重方法的综合权重方法.芬尼克狐优化算法的优化算法混合内核极端学习机器综合能源系统综合能源系统多种能源负载预测预测.

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科学领域:

  • 能源系统工程 能源系统工程
  • 人工智能的人工智能
  • 预测方法 预测方法

背景情况:

  • 综合能源系统 (IES) 对能源可持续性至关重要,但由于负载波动和众多影响因素,在准确的负载预测方面面临挑战.
  • 可靠的多变量负载预测对于IES的最佳调度和稳定的运行至关重要.

研究的目的:

  • 为IES开发一种先进的多能源负载预测方法.
  • 提高复杂能源系统负载预测的准确性和可靠性.

主要方法:

  • 使用综合权重方法,结合权和皮尔森相关系数来选择关键预测因素.
  • 使用混合内核极端学习机器 (ELM) 来建模多能负载之间的合关系.
  • 狐优化算法 (FFA) 用于优化ELM参数,减少预测随机性.

主要成果:

  • 拟议的方法在预测多种能源负载方面表现出高准确性,并与亚利桑那州立大学的数据进行了验证.
  • 获得的平均绝对误差 (MAE) 为0.0959,0.3103和0.0443.
  • 实现了0.1378,0.3848和0.0578的根平均平方误差 (RMSE),加权平均绝对百分比误差 (WMAPE) 为1.915%.

结论:

  • 结合的FFA和混合内核ELM方法显著提高了IES的多能负载预测准确性.
  • 与其他模型相比,该方法可以大幅减少MAE,RMSE和WMAPE.
  • 这种方法为优化IES运行和推进能源可持续性提供了可靠的工具.