Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

152
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
152
Load-frequency control01:28

Load-frequency control

113
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
113
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

91
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.
91
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

71
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
71
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

509
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
509
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

152
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
152

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Targeted Therapy and Oral Chemotherapy as Maintenance Treatment in Pediatric Very-High-Risk and High-Risk Rhabdomyosarcoma: A Retrospective Study of Efficacy and Safety.

International journal of cancer·2026
Same author

NAPRT expression and epigenetic regulation in pediatric rhabdomyosarcoma as a potential biomarker for NAMPT inhibition.

Molecular cancer therapeutics·2026
Same author

A non-parametric adaptive conformal inference based probabilistic hour-ahead solar PV power forecasting method.

Scientific reports·2026
Same author

Association Between Sarcopenia and Frailty Transition in Chinese Older Adults: A Multistate Markov Model Study.

Journal of the American Medical Directors Association·2026
Same author

Mismatch repair deficiency drives malignant progression and alters the tumor immune microenvironment in glioblastoma models.

The Journal of clinical investigation·2025
Same author

Comparative evaluation and simulation of blockchain consensus mechanisms for secure and scalable peer to peer energy trading in microgrids.

Scientific reports·2025

Related Experiment Video

Updated: May 24, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

472

Electrical load forecasting in power systems based on quantum computing using time series-based quantum artificial

Mohammad Reza Habibi1, Saeed Golestan2, Yanpeng Wu3

  • 1AAU Energy, Aalborg University, Aalborg, Denmark. mre@energy.aau.dk.

Scientific Reports
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

This study uses a hybrid quantum/classical artificial neural network for short-term load forecasting in power systems. The quantum computing approach accurately predicts future load values using only historical data, enhancing energy management strategies.

Keywords:
Artificial intelligenceArtificial neural networksLoad forecastingQuantum computingResidential loadSmart grid

More Related Videos

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

176
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.4K

Related Experiment Videos

Last Updated: May 24, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

472
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

176
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.4K

Area of Science:

  • Artificial Intelligence
  • Quantum Computing
  • Power Systems Engineering

Background:

  • Reliable power system operation necessitates precise energy management, challenged by unpredictable consumer behavior and load uncertainties.
  • Accurate load forecasting is crucial for efficient energy management, reducing complexity and improving system reliability.
  • Existing forecasting methods often struggle with inherent uncertainties in power system data.

Purpose of the Study:

  • To implement a quantum computing-based artificial neural network for accurate short-term load forecasting.
  • To evaluate a hybrid quantum/classical approach for predicting future load values.
  • To demonstrate the potential of quantum artificial intelligence in addressing forecasting challenges within smart grids.

Main Methods:

  • A hybrid quantum/classical artificial neural network was developed for load forecasting.
  • A time series-based technique was employed, utilizing only historical load data.
  • The model was tested on two distinct load types from an experimental laboratory setting.

Main Results:

  • The quantum computing-based strategy successfully predicted future load values.
  • The hybrid model demonstrated effectiveness in short-term load forecasting.
  • Experimental results validated the accuracy of the quantum-enhanced approach.

Conclusions:

  • Quantum computing-based artificial intelligence shows significant potential for forecasting applications in smart grids.
  • The hybrid quantum/classical neural network offers a promising solution for managing uncertainties in power system load forecasting.
  • This approach enhances energy management by providing reliable predictions based solely on historical load data.