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

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Maximum Power Transfer01:16

Maximum Power Transfer

Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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.
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

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 power flow program computes the...
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...

You might also read

Related Articles

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

Sort by
Same author

Enhancing educational environments: A sustainable approach through creative design and upcycling applications.

Scientific reports·2026
Same author

Comparing machine learning and deep learning approaches to predicting the seismic response of slab-column connections.

Scientific reports·2026
Same author

A novel quantum motivated particle swarm method with enhanced search strategy for electromagnetic optimization problems.

Scientific reports·2026
Same author

Comparison between various approaches without weighting factors with conventional MPTC for linear induction motors.

Scientific reports·2026
Same author

Parameters optimization of photovoltaic systems using modified quantum inspired particle swarm method.

Scientific reports·2026
Same author

Adaptive Control-based frequency control strategy for PV/ DEG/ battery power system during islanding conditions.

Scientific reports·2025
Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026
Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026
Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026
Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026
Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026
Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems.

Samir A Hamad1, Mohamed A Ghalib1, Amr Munshi2

  • 1Process Control Technology Department, Faculty of Technology and Education, Beni-Suef University, Beni Suef, Egypt.

Scientific Reports
|March 29, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can optimize standalone Photovoltaic (PV) systems by accurately predicting maximum power point (MPP). Decision Tree Regression (DTR) demonstrated superior performance in forecasting PV system parameters under varying conditions.

Keywords:
Artificial neural networkDC–DC converterMachine-learningMaximum power extraction (MPE) techniquePrediction model

More Related Videos

Comparative Study of Simulation of Temperature Rise in Ring Main Unit
04:35

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jul 2, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Comparative Study of Simulation of Temperature Rise in Ring Main Unit
04:35

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

Published on: July 5, 2024

Area of Science:

  • Renewable Energy Systems
  • Machine Learning Applications
  • Power Electronics

Background:

  • Photovoltaic (PV) systems exhibit nonlinear power generation, challenging effective data management.
  • Fluctuating weather conditions necessitate advanced methods for optimizing PV system performance.
  • Machine learning (ML) offers a promising approach to enhance the efficiency of PV systems by tracking their maximum power point (MPP).

Purpose of the Study:

  • To develop and evaluate machine learning models for tracking the MPP of standalone PV systems.
  • To compare the predictive accuracy of various ML algorithms in forecasting PV system parameters.
  • To identify key factors influencing the performance of ML models in PV system optimization.

Main Methods:

  • Exploration of ML algorithms including Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN).
  • Utilizing PV unit technical specifications, irradiance, and temperature data to predict maximum power, current, and voltage.
  • Simulation conducted on a 100 kW solar panel; performance evaluated using Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Absolute Error (MAE).

Main Results:

  • Decision Tree Regression (DTR) significantly outperformed other tested ML algorithms in predicting maximum current (Im), voltage (Vm), and power (Pm).
  • The DTR model achieved high accuracy with RMSE, MAE, and R2 values of 0.006, 0.004, and 0.99999 for Im; 0.015, 0.0036, and 0.99999 for Vm; and 2.36, 0.871, and 0.99999 for Pm.
  • Feature importance analysis indicated that training dataset size, operating conditions, model type, and data preprocessing significantly impact prediction accuracy.

Conclusions:

  • Machine learning, particularly Decision Tree Regression, provides an effective solution for optimizing standalone PV systems by accurately tracking the MPP.
  • The developed ML models can forecast essential PV parameters and inform boost converter duty cycle adjustments for improved energy harvesting.
  • Further research should consider the influence of dataset size, operating conditions, and preprocessing techniques for enhanced predictive performance in PV energy systems.