Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation fore...
Saved in:
Main Authors: | Mohamed Massaoudi, Haitham Abu-Rub, Shady S. Refaat, Mohamed Trabelsi, Ines Chihi, Fakhreddine S. Oueslati |
---|---|
Format: | article |
Language: | EN |
Published: |
IEEE
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/5b9c140af32240588b39dec97d3d223c |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
by: Stefano Frizzo Stefenon, et al.
Published: (2021) -
Temperature Effect on Photovoltaic Modules Power Drop
by: Qais Mohammed Aish
Published: (2015) -
A Stacking Ensemble Model to Predict Daily Number of Hospital Admissions for Cardiovascular Diseases
by: Zhixu Hu, et al.
Published: (2020) -
Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
by: Esteban Sánchez, et al.
Published: (2021) -
Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change
by: Pavel Matrenin, et al.
Published: (2022)