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Related Experiment Videos

An Uncertainty-Aware Temporal Transformer for Probabilistic Interval Modeling in Wind Power Forecasting.

Shengshun Sun¹, Meitong Chen², Mafangzhou Mo²

¹School of Automation, Qingdao University, Qingdao 266071, China.

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

This study introduces an uncertainty-aware temporal transformer for wind power forecasting, improving accuracy and reliability in renewable energy systems. The novel framework effectively models power output uncertainty for better grid management.

Keywords:

AI-driven data analytics data mining for energy systems deep learning for power systems temporal transformer wind power forecasting

Related Experiment Videos

Area of Science:

Energy Systems
Artificial Intelligence
Meteorology

Background:

High renewable energy integration presents significant wind power forecasting challenges due to inherent randomness and uncertainty.
Conventional point-forecast methods are inadequate for risk-aware power system scheduling.

Purpose of the Study:

To develop an uncertainty-aware temporal transformer framework for wind power forecasting.
To integrate probabilistic modeling with deep temporal representation learning for enhanced prediction and uncertainty characterization.

Main Methods:

Modularizing uncertainty quantification within the attention mechanism.
Employing a probability-driven temporal attention mechanism for feature aggregation.
Utilizing a multi-quantile output and interval modeling strategy for direct conditional distribution learning.

Main Results:

The proposed method outperforms traditional and deterministic transformer models, achieving MAE of 0.089, RMSE of 0.132, and MAPE of 10.84%.
Attained a Prediction Interval Coverage Probability (PICP) of 0.91 with a Mean Prediction Interval Width (MPIW) of 0.221 and Coverage Weighted Cost (CWC) of 0.241.
Demonstrated adaptive heteroscedastic interval generation, effectively capturing uncertainty during high-volatility events.

Conclusions:

The framework provides accurate point and interval wind power forecasts with statistical confidence.
Effectively balances coverage reliability and interval compactness, outperforming mainstream probabilistic methods.
Offers a robust solution for reliable power system scheduling under high renewable energy penetration.