Data-Driven Multiple ARIMA Through Neural Fusion for Enhanced Wind Power Prediction With Missing Data

Data-Driven Multiple ARIMA Through Neural Fusion for Enhanced Wind Power Prediction With Missing Data

Robust wind power forecasting in the presence of missing data remains a critical challenge for efficient grid management and energy trading. This paper proposes a novel methodology that integrates a data-driven approach for creating multiple Autoregressive Integrated Moving Average (ARIMA) models wi...

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Bibliographic Details
Main Authors: Xiaoou Li, Wen Yu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Multiple models
neural networks
missing data
ARIMA
Online Access:https://ieeexplore.ieee.org/document/11119407/
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https://ieeexplore.ieee.org/document/11119407/

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