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

164
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:
164
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

93
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.
93
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

161
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...
161
Control of Power Flow01:30

Control of Power Flow

250
There are several methods to control power flow in power systems:
250
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

529
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...
529
Power in a Three-Phase Circuit01:15

Power in a Three-Phase Circuit

289
Three-phase systems have two configurations: the wye and delta. A star configuration can be three or four wires; in a delta configuration, the components are connected in a closed loop. Instantaneous power refers to the power value at a precise moment, and in a balanced three-phase system, it is constant. This is because the sum of the instantaneous powers in the three phases remains steady over time, despite individual fluctuations, due to the symmetry and phase relationship. The total...
289

You might also read

Related Articles

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

Sort by
Same author

Measuring Heat Stress for Human Health in Cities: A Low-Cost Prototype Tested in a District of Valencia, Spain.

Sensors (Basel, Switzerland)·2023
Same author

PEMFCs Model-Based Fault Diagnosis: A Proposal Based on Virtual and Real Sensors Data Fusion.

Sensors (Basel, Switzerland)·2023
Same author

Light electric vehicle charging strategy for low impact on the grid.

Environmental science and pollution research international·2020
Same author

Low-cost web-based Supervisory Control and Data Acquisition system for a microgrid testbed: A case study in design and implementation for academic and research applications.

Heliyon·2019

Related Experiment Video

Updated: May 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement.

Dácil Díaz-Bello1, Carlos Vargas-Salgado2,3, Manuel Alcazar-Ortega1,4

  • 1Instituto de Ingeniería Energética, Universitat Politècnica de València, Valencia, Spain.

Scientific Reports
|January 27, 2025
PubMed
Summary

Accurate photovoltaic power prediction is crucial for renewable energy management. This study introduces a novel method using genetic algorithms and dynamic neural networks to enhance solar power forecasting accuracy, achieving significant improvements.

Keywords:
Artificial neural networksForecastingGenetic algorithmsModelingOptimizationParameterizationPhotovoltaic energy

More Related Videos

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

455
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

219

Related Experiment Videos

Last Updated: May 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
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

455
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

219

Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Photovoltaic Power Generation

Background:

  • Efficient management of energy systems relies on accurate photovoltaic (PV) power generation predictions.
  • Despite advancements in weather forecasting, precise PV power prediction remains a significant challenge.
  • The inherent uncertainty of renewable energy sources necessitates reliable forecasting methods.

Purpose of the Study:

  • To develop and validate a novel approach for optimizing photovoltaic power prediction.
  • To enhance the accuracy of solar power forecasting by dynamically refining neural network structures.
  • To minimize prediction errors using a combination of genetic algorithms and neural network optimization.

Main Methods:

  • A novel methodology combining genetic algorithms and dynamic neural network structure refinement was employed.
  • Neural network parameters, including neurons, transfer functions, weights, and biases, were dynamically adjusted during training.
  • The approach was evaluated using annual, monthly, and seasonal data over twelve representative days, with comparisons to multiple linear regression and nonlinear autoregressive neural network models.
  • MATLAB was utilized for modeling, training, and testing, with validation on a real 4.2 kW PV plant.

Main Results:

  • The proposed method demonstrated significant improvements in prediction accuracy compared to existing models.
  • Evaluation metrics including mean square error (MSE), R-value, and mean percentage error indicated promising results.
  • Achieved low mean square errors of 20 W on cloudy days and 175 W on sunny days.
  • High prediction versus target regression consistency was observed, with R values ranging from 0.95824 to 0.99980.

Conclusions:

  • The novel approach effectively enhances the reliability and accuracy of photovoltaic power generation predictions.
  • Dynamic adjustment of neural network parameters is a viable strategy for optimizing solar power forecasting.
  • The methodology offers a significant advancement in managing energy systems with high renewable energy penetration.