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相关概念视频

Electrical Energy01:10

Electrical Energy

1.2K
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.
1.2K
Electrical Power01:07

Electrical Power

3.1K
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...
3.1K
Power and Energy01:12

Power and Energy

730
The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
730
Power System Distribution01:25

Power System Distribution

229
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
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Insulation Coordination01:23

Insulation Coordination

127
Insulation coordination is the process of matching electric equipment's insulation strength with protective device characteristics to protect the equipment against expected overvoltages. This selection is based on engineering judgment and cost. Equipment can generally withstand short-duration high transient overvoltages, but repeated tests with identical waveforms can yield inconsistent results. As a result, standard impulse voltage waveforms are used for testing, defined by specific times...
127
Energy and Power Signals01:17

Energy and Power Signals

277
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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相关实验视频

Updated: Jun 20, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
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Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

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开放式协作智能插头用于能源管理.

Almir Neto1, Luis Gomes2, Zita Vale2

  • 1Federal Institute of Education, Science and Technology of Maranhão (IFMA), Av. Getúlio Vargas, n 04, Monte Castelo, São Luís, MA, 65030-005, Brazil.

HardwareX
|July 23, 2024
PubMed
概括

一个新的智能插头,环境意识智能插头 (EnAPlug),使用微型机器学习 (Tiny ML) 来预测冰箱的湿度和运动活动. 这可以通过本地数据存储和智能家居自动化实现增强的能源管理.

关键词:
边缘冲动是一种冲动.能源分析仪 能源分析仪物联网的物联网,就是物联网.机器学习 机器学习智能插头是一种智能插头.

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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相关实验视频

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科学领域:

  • 家电和家庭自动化
  • 机器学习应用 机器学习应用
  • 能源管理系统 能源管理系统

背景情况:

  • 在家用自动化中智能设备的普及需要综合能源管理.
  • 了解环境和能源参数之间的相互作用对于高效的设备操作至关重要.
  • 现有的智能插头缺乏用于高级分析的本地数据存储功能.

研究的目的:

  • 提出一个环境意识智能插头 (EnAPlug),整合机器学习以提高环境意识.
  • 为了证明EnAPlug在预测冰箱内部湿度和电机激活方面的能力.
  • 通过本地化数据处理来探索增强能源管理的潜力.

主要方法:

  • 使用微型机器学习 (Tiny ML) 开发一个环境意识智能插头 (EnAPlug).
  • 将EnAPlug应用于冰箱,以实时收集环境和操作数据.
  • 实施内部湿度和电机激活的预测模型 (5分钟前预测).

主要成果:

  • 湿度预测模型实现了0.055和0.058的根平均平方误差 (RMSE),R2分数为0.97和0.99.
  • 运动激活预测模型显示高精度 (94.74%-94.84%) 和F1得分 (0.93-0.97).
  • EnAPlug原型成功实现了本地数据存储,使其与传统的智能插头有所区别.

结论:

  • EnAPlug原型在环境参数预测和运营预测方面显示出有希望的结果.
  • 当地数据存储能力增强了复杂的设备上能源管理策略的潜力.
  • 在智能插头中集成Tiny ML为更智能和更高效的家用自动化提供了途径.