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相关概念视频

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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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Electrical Energy01:10

Electrical Energy

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Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
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Electrical Power01:07

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Electric power is the product of current and voltage, represented in units of joules per second, or watts. For example, cars often have one or more auxiliary power outlets with which you can charge a cell phone or other electronic devices. These outlets may be rated at 20 amps and 12 volts, so that the circuit can deliver a maximum power of 240 watts. Consider a 25 Watt bulb and a 60 Watt bulb. The conversion of electrical energy produces heat and light, while the kinetic energy lost by the...
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Maximum Power Flow and Line Loadability01:23

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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 Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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相关实验视频

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使用多个人工智能模型和交叉验证技术模拟电力消耗数据.

Mariam Hosny1, Omnia Abu Waraga2, Manar Abu Talib2

  • 1Department of Civil and Environmental Engineering, University of Sharjah, United Arab Emirates.

Data in brief
|November 29, 2023
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概括
此摘要是机器生成的。

本研究介绍了迪拜 (2017年5月至2019年12月) 的电力消耗数据,并应用机器学习模型进行准确的预测. 目标是通过有效预测未来的电力需求来减少能源浪费.

关键词:
人工神经网络的人工神经网络通过交叉验证验证.预测电力消耗的预测机器学习模型的机器学习模型

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科学领域:

  • 能源管理 能源管理
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 全球电力生产往往超过消费,导致大量的财务和能源浪费.
  • 准确的电力消耗预测对于优化资源分配和减少损失至关重要.

研究的目的:

  • 介绍阿联迪拜每月社区电力消耗的综合数据集 (2017年5月至2019年12月).
  • 评估各种机器学习模型在预测电力消耗方面的性能.
  • 分析数据格式和大小对预测准确性的影响.

主要方法:

  • 从迪拜脉冲收集的月度电力消耗数据和迪拜统计中心和迪拜国际机场的人口/环境数据.
  • 设计了额外的功能,如外籍人士比例和建筑物占用率.
  • 实施并比较多个机器学习模型,包括线性回归变体,SVM,决策树,集合模型和神经网络.
  • 训练模型在临时排序和随机分割的数据集,不同的测试数据大小,并采用滚动和移动交叉验证 (CV) 方法.

主要成果:

  • 使用R平方,根平均平方误差,平均绝对误差和计算时间等指标评估模型性能.
  • 通过滚动和移动CV程序评估模型的可靠性.
  • 确定了模型精度与训练数据量之间的依赖性.

结论:

  • 提出的数据集和模型评估为准确预测迪拜的电力消耗提供了基础.
  • 这些发现可以为减轻能源浪费和优化资源管理的战略提供信息.
  • 这些数据可以进一步用于分析COVID-19等事件对电力使用模式的影响.