COMPARISON OF MODERN METHODS OF MODELING POWER LOSSES

The fight against electricity losses in distribution networks is a key area of energy efficiency improvement. The article discusses modern methods of modeling power losses: machine learning and artificial intelligence, the Monte Carlo method, as well as the use of the Internet of Things (IoT) and smart meters. These methods are analyzed according to their principles of operation, advantages, disadvantages and application in the energy sector. The article emphasizes that the combined use of these technologies can significantly improve management efficiency and minimize power losses.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-28
DOI
https://doi.org/10.5281/zenodo.14237517
Primary Topic
Engineering and Agricultural Innovations
Type
article
Field-Weighted Citation Impact
0.00
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article

COMPARISON OF MODERN METHODS OF MODELING POWER LOSSES

Zenodo (CERN European Organization for Nuclear Research)
Engineering and Agricultural Innovations
article

COMPARISON OF MODERN METHODS OF MODELING POWER LOSSES

article en

Abstract

The fight against electricity losses in distribution networks is a key area of energy efficiency improvement. The article discusses modern methods of modeling power losses: machine learning and artificial intelligence, the Monte Carlo method, as well as the use of the Internet of Things (IoT) and smart meters. These methods are analyzed according to their principles of operation, advantages, disadvantages and application in the energy sector. The article emphasizes that the combined use of these technologies can significantly improve management efficiency and minimize power losses.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 39%
Engineering and Agricultural Innovations
DOIOpen Access PDFOpenAlex
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COMPARISON OF MODERN METHODS OF MODELING POWER LOSSES · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS