Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254570" target="_blank" >RIV/61989100:27240/23:10254570 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1996-1073/16/10/4060" target="_blank" >https://www.mdpi.com/1996-1073/16/10/4060</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/en16104060" target="_blank" >10.3390/en16104060</a>

Result language

angličtina

Original language name

Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Original language description

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems&apos; reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class&apos;s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach&apos;s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Energies

ISSN

1996-1073

e-ISSN

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Volume of the periodical

16

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

29

Pages from-to

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UT code for WoS article

000996871600001

EID of the result in the Scopus database

2-s2.0-85160598295