Power Load Forecasting Using Machine Learning Methods

Result code in 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>
Alternative codes found
RIV/61989100:27730/25:10260004
Result on the web
<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>

Result language
angličtina
Original language name
Power Load Forecasting Using Machine Learning Methods
Original language description
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.
Czech name
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Czech description
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Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Smart Innovation, Systems and Technologies. Volume 466
ISBN
978-3-032-13047-1
ISSN
2190-3018
e-ISSN
2190-3026
Number of pages
14
Pages from-to
57-70
Publisher name
Springer
Place of publication
Cham
Event location
Ostrava
Event date
Jul 21, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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