Work

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

Year: 2025

Type: article

Abstract: 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 with with neural network-based fusion strategy. We first employ statistical properties to identify distinct operational regimes within the wind power data, training specialized ARIMA models. Then, we introduce neural networks for the robust fusion of these segmented ARIMA forecasts. These networks are engineered to: 1) learn and exploit intricate relationships between the predictions originating from different operational regimes, and 2) dynamically adapt its fusion strategy based on the availability of current data and the predicted reliability of each ARIMA model, effectively compensating for missing data points. Extensive experiments conducted on real-world wind power datasets characterized by varying missing data patterns demonstrate the superior accuracy and robustness of our proposed approach compared to conventional forecasting methods.

Source: IEEE Access

DOI: 10.1109/access.2025.3596855

Authors: Xiaoou Li, Wen Yu

Institutions: Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Instituto Politécnico Nacional

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075