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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Energies

ISSN

1996-1073

e-ISSN

—

Svazek periodika

16

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

29

Strana od-do

—

Kód UT WoS článku

000996871600001

EID výsledku v databázi Scopus

2-s2.0-85160598295