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

Electrical Energy01:10

Electrical Energy

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. The...
Batteries and Fuel Cells03:12

Batteries and Fuel Cells

A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
Induced Electric Fields: Applications01:27

Induced Electric Fields: Applications

An important distinction exists between the electric field induced by a changing magnetic field and the electrostatic field produced by a fixed charge distribution. Specifically, the induced electric field is nonconservative because it does not work in moving a charge over a closed path. In contrast, the electrostatic field is conservative and does no net work over a closed path. Hence, electric potential can be associated with the electrostatic field but not the induced field. The following...
Continuous Charge Distributions01:17

Continuous Charge Distributions

Imagine a bucket of water. It contains many molecules, of the order of 1026 molecules. Thus, although it contains discrete elements (molecules) at the microscopic level, macroscopically, it can be considered continuous. Small volume elements of water, infinitesimal compared to the bulk of the bucket's volume, still contain many molecules. Under this framework, quantized matter is approximated as continuous for practical purposes.
The electric charge can also be subjected to an analogical...
Electrochemical Systems01:24

Electrochemical Systems

Electrochemical systems provide a fascinating insight into the dynamic interplay of charged species within various phases. One notable example is the interaction between a membrane permeable to K⁺ ions but not to Cl⁻ ions, separating an aqueous KCl solution from pure water. As K⁺ ions diffuse through the membrane, they generate net charges on each phase, leading to a potential difference between them.Similarly, when a piece of Zn is immersed in an aqueous ZnSO₄ solution, the Zn metal, composed...
Energy and Power Signals01:17

Energy and Power Signals

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:

You might also read

Related Articles

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

Sort by
Same author

AI-driven cybersecurity framework for anomaly detection in power systems.

Scientific reports·2025
Same author

Ambient oscillatory mode assessment in power system using an advanced signal processing method.

ISA transactions·2023
See all related articles

Related Experiment Videos

An integrated machine learning framework for EV charging management.

Nandith Sreekumar1, Rahul Satheesh2, G S Asha Rani3

  • 1Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India.

Scientific Reports
|May 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework to manage electric vehicle (EV) charging, optimizing grid stability and user services. It predicts energy needs, forecasts demand, and segments users for better electric mobility management.

Keywords:
Charging behaviorCustomer segmentationDemand forecastingElectric vehiclesMachine learningSmart grid

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • The rapid adoption of electric vehicles (EVs) presents significant challenges to existing power grid stability.
  • Unpredictable EV charging patterns can lead to grid instability and operational inefficiencies.

Purpose of the Study:

  • To develop a multi-layered machine learning framework for optimizing electric vehicle charging.
  • To balance grid stability with user service requirements in electric mobility.
  • To provide data-driven insights for utility providers and charge point operators.

Main Methods:

  • Session-level prediction models (XGBoost, Random Forest) for energy and cost estimation.
  • Station-level forecasting model (XGBoost) for daily EV demand prediction.
  • K-Means clustering for segmenting EV user behavior.

Main Results:

  • XGBoost achieved high accuracy in energy prediction ([Formula: see text]) and cost prediction ([Formula: see text]).
  • The XGBoost forecasting model demonstrated high precision for daily demand (MAE=0.90 kWh).
  • K-Means clustering identified key user segments: Heavy Energy Users (43.5%) and Occasional Visitors (38.8%).

Conclusions:

  • The proposed framework effectively integrates prediction, forecasting, and user segmentation for scalable EV charging management.
  • Insights enable personalized services and demand response strategies for Charge Point Operators.
  • The study equips stakeholders with tools for proactive load congestion management and optimized capital expenditure planning.