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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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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Electrical Energy01:10

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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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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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Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

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Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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Related Experiment Video

Updated: Dec 21, 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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Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart

Tuong Le1,2, Minh Thanh Vo3, Tung Kieu4

  • 1Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam.

Sensors (Basel, Switzerland)
|May 13, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for Multiple Electric Energy Consumption (MEC) forecasting in smart buildings. It uses Transfer Learning and Long Short-Term Memory (TLL) to reduce computation time and improve energy management.

Keywords:
intelligent energy management systemlong short-term memory networksmultiple electric energy consumption forecastingthe cluster-based strategy for transfer learningtransfer learning

Related Experiment Videos

Last Updated: Dec 21, 2025

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
05:50

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Area of Science:

  • Energy Management
  • Artificial Intelligence
  • Smart Buildings

Background:

  • Accurate electric energy consumption forecasting is crucial for smart building efficiency.
  • Existing predictive models are resource-intensive for multiple profiles.
  • Need for efficient forecasting methods for diverse energy consumption patterns.

Purpose of the Study:

  • To develop a robust framework for Multiple Electric Energy Consumption (MEC) forecasting.
  • To reduce computational time and resource usage in smart buildings.
  • To enhance intelligent energy management systems.

Main Methods:

  • Developed the MEC-TLL framework using Transfer Learning and Long Short-Term Memory (TLL).
  • Employed k-means clustering and Silhouette analysis for optimal data grouping.
  • Implemented a cluster-based strategy for efficient model training.

Main Results:

  • The MEC-TLL framework significantly reduced computational time.
  • Achieved superior performance metrics compared to existing methods.
  • Demonstrated economical overheads with enhanced forecasting accuracy.

Conclusions:

  • The proposed MEC-TLL framework offers an effective solution for multiple electric energy consumption forecasting.
  • The approach is suitable for intelligent energy management in smart buildings.
  • Validated through extensive experiments on smart buildings in South Korea.