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

Sustainable Development01:43

Sustainable Development

13.2K
As the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
13.2K
Energy and Power Signals01:17

Energy and Power Signals

254
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:
254
Power and Energy01:12

Power and Energy

662
The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
662
Energy Line and Hydraulic Gradient Line01:27

Energy Line and Hydraulic Gradient Line

682
Based on Bernoulli's equation, the energy line (EL) and hydraulic grade line (HGL) provide graphical representations of energy distribution in a fluid flow system. For steady, incompressible, inviscid flows, Bernoulli's equation is expressed as:
682
Electrical Energy01:10

Electrical Energy

1.2K
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.
1.2K
Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

635
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
635

You might also read

Related Articles

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

Sort by
Same author

Predictive uncertainty in state-estimation drives active sensing.

Bioinspiration & biomimetics·2024
Same author

Machine learning-based exploration of biochar for environmental management and remediation.

Journal of environmental management·2024
Same author

Halide Perovskites for Photoelectrochemical Water Splitting and CO<sub>2</sub> Reduction: Challenges and Opportunities.

ACS catalysis·2024
Same author

Phytochemicals in Pancreatic Cancer Treatment: A Machine Learning Study.

ACS omega·2024
Same author

A reliability study on the cumulative averaging method for estimating effective stimulus time in vibration studies.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2023
Same author

Analysis of CH<sub>4</sub> Uptake over Metal-Organic Frameworks Using Data-Mining Tools.

ACS combinatorial science·2019
Same journal

3-Methyleneazetidine: a versatile building block for functional and post-modifiable polysulfonamides.

Chemical communications (Cambridge, England)·2026
Same journal

Synthesis of divalent galactosyl and fucosyl spiropyran derivatives for the targeted inhibition of bacterial biofilms.

Chemical communications (Cambridge, England)·2026
Same journal

Emergent cytotoxicity and mitochondrial alterations induced by a heterobimetallic Re(I)/Au(I) complex.

Chemical communications (Cambridge, England)·2026
Same journal

Cyanoacetylation of amines <i>via</i> a traceless cyanoacetyl radical: synthetic access to teriflunomide.

Chemical communications (Cambridge, England)·2026
Same journal

Loading layered double hydroxide nanoarray catalysts on a micro-curved substrate for kinetics-favorable water electrolysis reaction.

Chemical communications (Cambridge, England)·2026
Same journal

Bridging <i>in situ</i> measurements and practical conditions through gas-liquid management for CO/CO<sub>2</sub> reduction.

Chemical communications (Cambridge, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 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

484

Machine learning for a sustainable energy future.

Burcu Oral1, Ahmet Coşgun1, Aysegul Kilic1

  • 1Department of Chemical Engineering, Boğaziçi University, 34342, Bebek-Istanbul, Turkey. yildirra@bogazici.edu.tr.

Chemical Communications (Cambridge, England)
|December 20, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) can significantly advance sustainable energy efforts and United Nations Sustainable Development Goals (SDGs). Despite challenges like high energy consumption, ML offers solutions for energy production, storage, and planning.

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K

Related Experiment Videos

Last Updated: Jun 4, 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

484
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K

Area of Science:

  • Sustainable energy and its intersection with global development goals.
  • The application of machine learning (ML) as a transformative technology.

Background:

  • Energy production is vital for human activities but is a primary driver of global warming.
  • Sustainable energy is crucial for achieving most United Nations Sustainable Development Goals (SDGs), particularly SDG7.

Purpose of the Study:

  • To analyze the potential role of machine learning (ML) in advancing sustainable energy.
  • To explore ML's contribution to achieving the UN SDGs.

Main Methods:

  • Reviewing the current applications of ML in energy production and storage.
  • Examining ML's use in energy forecasting and planning.
  • Identifying challenges and opportunities for ML in sustainable energy.

Main Results:

  • ML can contribute to sustainable energy through data collection via monitoring and remote sensing.
  • ML aids in planning global sustainable energy initiatives.
  • ML can enhance the performance of novel sustainable energy technologies.

Conclusions:

  • Despite challenges such as high energy consumption and potential for increased inequality, ML offers significant opportunities for sustainable energy.
  • ML is a key enabler for achieving sustainable energy targets and broader UN SDGs.