Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting
Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisi...
Enregistré dans:
Auteurs principaux: | Pedro M. R. Bento, Jose A. N. Pombo, Maria R. A. Calado, Silvio J. P. S. Mariano |
---|---|
Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/dd27aaef327f4061b5d5426f0f4a3a92 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
FORECASTING SAVING DEPOSIT IN MALAYSIAN ISLAMIC BANKING: COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK AND ARIMA
par: Raditya Sukmana, et autres
Publié: (2014) -
Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
par: Félix Tena, et autres
Publié: (2021) -
Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans
par: Rohit Kundu, et autres
Publié: (2021) -
Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
par: Stefano Frizzo Stefenon, et autres
Publié: (2021) -
Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
par: Jian Sun, PhD
Publié: (2021)