Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting
Abstract Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale...
Saved in:
Main Authors: | Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-025-86982-0 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting
by: Krzysztof Gajowniczek, et al.
Published: (2018-01-01) -
Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting
by: Winita Sulandari, et al.
Published: (2025-01-01) -
Using crafted features and polar bear optimization algorithm for short-term electric load forecast system
by: Mansi Bhatnagar, et al.
Published: (2025-01-01) -
Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid
by: N. A. Serebryakov
Published: (2021-03-01) -
A New Strategy for Short-Term Load Forecasting
by: Yi Yang, et al.
Published: (2013-01-01)