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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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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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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

Electrical Power

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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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Generation of Three-Phase Voltage01:21

Generation of Three-Phase Voltage

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A three-phase AC generator has a rotor with a rotating magnet placed within the stator mounted with the stationary three-phase winding to generate three-phase voltages via mutual induction. These windings are evenly distributed around the inner circumference of the stator and are arranged 120 electrical degrees apart. Three-phase stator windings consist of three separate coils or groups of coils, known as phases, each connected in Y (star) configuration or Delta configuration.
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Related Experiment Video

Updated: Oct 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

670

A deep LSTM network for the Spanish electricity consumption forecasting.

J F Torres1, F Martínez-Álvarez1, A Troncoso1

  • 1Data Science and Big Data Lab, Universidad Pablo de Olavide, 41013 Seville, Spain.

Neural Computing & Applications
|February 14, 2022
PubMed
Summary

Accurate short-term electricity demand forecasting is crucial for smart grid management. A Long Short-Term Memory (LSTM) network, optimized using the Coronavirus Optimization Algorithm (CVOA), achieved prediction errors below 1.5%.

Keywords:
Deep learningElectricity demandTime series forecasting

Related Experiment Videos

Last Updated: Oct 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

670

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Electricity is essential for modern societies, and increasing consumption necessitates efficient smart grid management.
  • Accurate short-term electricity forecasting is vital for grid stability, sustainability, and safety.
  • Traditional forecasting methods often struggle with the complex, sequential nature of electricity consumption data.

Purpose of the Study:

  • To propose and evaluate a deep neural network for short-term electricity consumption forecasting.
  • To optimize a Long Short-Term Memory (LSTM) network using a novel metaheuristic algorithm.
  • To compare the LSTM model's performance against various deep learning and traditional machine learning techniques.

Main Methods:

  • A Long Short-Term Memory (LSTM) network, suitable for time-series data, was employed for electricity demand prediction.
  • Hyper-parameter optimization for the LSTM was performed using both random search and the Coronavirus Optimization Algorithm (CVOA).
  • The optimized LSTM model was applied to predict electricity demand with a 4-hour forecast horizon using extensive Spanish electricity consumption data (9.5 years at 10-min frequency).

Main Results:

  • The LSTM network optimized with the Coronavirus Optimization Algorithm (CVOA) demonstrated superior performance in electricity demand forecasting.
  • The proposed LSTM model achieved the lowest prediction error, falling below 1.5%.
  • Performance was benchmarked against deep neural networks (Deep Feed-Forward Neural Network, Temporal Fusion Transformers) and traditional methods (Linear Regression, Decision Trees, Gradient-Boosted Trees, Random Forest).

Conclusions:

  • The Coronavirus Optimization Algorithm (CVOA) is an effective metaheuristic for optimizing LSTM hyper-parameters in electricity forecasting.
  • The proposed LSTM model offers a highly accurate and reliable solution for short-term electricity demand prediction.
  • This research contributes to the development of more efficient and sustainable smart grid management systems through advanced forecasting techniques.