Power Load Forecasting Using Machine Learning Methods
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10260004" target="_blank" >RIV/61989100:27230/25:10260004 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27730/25:10260004
Výsledek na webu
<a href="https://link.springer.com/chapter/10.1007/978-3-032-13048-8_5" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-032-13048-8_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-032-13048-8_5" target="_blank" >10.1007/978-3-032-13048-8_5</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Power Load Forecasting Using Machine Learning Methods
Popis výsledku v původním jazyce
Short-term power load forecasting (STPLF) is essential to the power grid system. Forecasting the power load process can help reduce electricity production costs and optimize power quality. Many studies are in this field, but it is necessary to improve the accuracy of the forecast. For example, forecasting short-term demand power is essential for operating an off-grid system. This article tested three models for forecasting power load in the short term: Decision Tree (DT), Boosting DT, and Bagging DT. The data set was measured in the off-grid labs at the VSB-Technical University of Ostrava, Czech Republic. The data set was clustered using k-means clustering, and the distance between the samples and each cluster was measured, which was fed to the forecast stage. Using three new space features reduced the computation complexity of the training and testing phases. The performance of the designed models was evaluated using the root mean square error (RMSE), mean absolute percentage error (MAPE), and R<sup>2</sup>. The Bagging DT achieved the lowest forecast error, followed by Boosting DT and standard DT. In contrast, the standard DT took a shorter time to test one sample than the ensemble DTs.
Název v anglickém jazyce
Power Load Forecasting Using Machine Learning Methods
Popis výsledku anglicky
Short-term power load forecasting (STPLF) is essential to the power grid system. Forecasting the power load process can help reduce electricity production costs and optimize power quality. Many studies are in this field, but it is necessary to improve the accuracy of the forecast. For example, forecasting short-term demand power is essential for operating an off-grid system. This article tested three models for forecasting power load in the short term: Decision Tree (DT), Boosting DT, and Bagging DT. The data set was measured in the off-grid labs at the VSB-Technical University of Ostrava, Czech Republic. The data set was clustered using k-means clustering, and the distance between the samples and each cluster was measured, which was fed to the forecast stage. Using three new space features reduced the computation complexity of the training and testing phases. The performance of the designed models was evaluated using the root mean square error (RMSE), mean absolute percentage error (MAPE), and R<sup>2</sup>. The Bagging DT achieved the lowest forecast error, followed by Boosting DT and standard DT. In contrast, the standard DT took a shorter time to test one sample than the ensemble DTs.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
—
Návaznosti
O - Projekt operacniho programu
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Smart Innovation, Systems and Technologies. Volume 466
ISBN
978-3-032-13047-1
ISSN
2190-3018
e-ISSN
2190-3026
Počet stran výsledku
14
Strana od-do
57-70
Název nakladatele
Springer
Místo vydání
Cham
Místo konání akce
Ostrava
Datum konání akce
21. 7. 2025
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—