Machine Learning for Energy Systems Optimization

This editorial overviews the contents of the Special Issue “Machine Learning for Energy Systems 2021” and review the trends in machine learning (ML) techniques for energy system (ES) optimization [...]

Saved in:
Bibliographic Details
Published inEnergies (Basel) Vol. 15; no. 11; p. 4116
Main Authors Kim, Insu, Kim, Beopsoo, Sidorov, Denis
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 03.06.2022
Subjects
Accuracy
Algorithms
Artificial intelligence
Batteries
Compressed air
Convergence
Costs
Electric power
Electrical equipment
Electricity distribution
Energy storage
Fibonacci numbers
Flywheels
Genetic algorithms
Heuristic methods
Learning algorithms
Linear programming
Lithium
Lithium ions
Machine learning
Mathematical models
Mathematical programming
Methods
Monte Carlo simulation
n/a
Objective function
Oil
Optimization
Problem solving
Scheduling
Search algorithms
Search methods
Sensitivity analysis
Statistical analysis
Storage batteries
Time series
Trends
Online AccessGet full text

Cover

More Information
Summary:This editorial overviews the contents of the Special Issue “Machine Learning for Energy Systems 2021” and review the trends in machine learning (ML) techniques for energy system (ES) optimization [...]
ISSN:1996-1073
1996-1073
DOI:10.3390/en15114116