A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting
It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensem...
Автори: | Qi Jiang, Yuxin Cheng, Haozhe Le, Chunquan Li, Peter X. Liu |
---|---|
Формат: | Стаття |
Мова: | English |
Опубліковано: |
MDPI AG
2022-07-01
|
Серія: | Mathematics |
Предмети: | |
Онлайн доступ: | https://www.mdpi.com/2227-7390/10/14/2446 |
Схожі ресурси
Схожі ресурси
-
Day-Ahead Forecast of Photovoltaic Power Based on a Novel Stacking Ensemble Method
за авторством: Luyao Liu, та інші
Опубліковано: (2023-01-01) -
Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study
за авторством: Qinghe Zhao, та інші
Опубліковано: (2023-12-01) -
Short-term load forecasting based on sample weights assignment
за авторством: Mingxu Xiang, та інші
Опубліковано: (2022-11-01) -
Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models
за авторством: Andrea Maria N. C. Ribeiro, та інші
Опубліковано: (2022-01-01) -
Past Vector Similarity for Short Term Electrical Load Forecasting at the Individual Household Level
за авторством: Haris Mansoor, та інші
Опубліковано: (2021-01-01)