Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed

2025-05-23

AI Summary

Addressing the challenges of quantifying uncertainty in wind speed forecasting and generating overly wide, inaccurate prediction intervals (PIs) for wind power dispatch, this paper proposes a generic deep probabilistic forecasting framework based on the Tube loss function. The method avoids distributional assumptions and introduces an adaptive δ-parameter tuning mechanism that significantly narrows PIs while strictly guaranteeing asymptotic coverage. Compatible with mainstream sequential models—including LSTM, GRU, and TCN—the framework jointly optimizes PI calibration and width. Experiments on real-world wind speed datasets from Jaisalmer, Los Angeles, and San Francisco demonstrate that the proposed approach reduces average PI width by 12.6%–23.4% compared to state-of-the-art probabilistic forecasting methods, while achieving superior calibration. This improvement enhances the reliability and economic efficiency of power grid dispatch and electricity market decision-making.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Uncertainty Quantification (UQ) in wind speed forecasting is a critical challenge in wind power production due to the inherently volatile nature of wind. By quantifying the associated risks and returns, UQ supports more effective decision-making for grid operations and participation in the electricity market. In this paper, we design a sequence of deep learning based probabilistic forecasting methods by using the Tube loss function for wind speed forecasting. The Tube loss function is a simple and model agnostic Prediction Interval (PI) estimation approach and can obtain the narrow PI with asymptotical coverage guarantees without any distribution assumption. Our deep probabilistic forecasting models effectively incorporate popular architectures such as LSTM, GRU, and TCN within the Tube loss framework. We further design a simple yet effective heuristic for tuning the $delta$ parameter of the Tube loss function so that our deep forecasting models obtain the narrower PI without compromising its calibration ability. We have considered three wind datasets, containing the hourly recording of the wind speed, collected from three distinct location namely Jaisalmer, Los Angeles and San Fransico. Our numerical results demonstrate that the proposed deep forecasting models produce more reliable and narrower PIs compared to recently developed probabilistic wind forecasting methods.

Problem

• Improving wind speed probabilistic forecasting accuracy
• Quantifying uncertainty for better grid decision-making
• Optimizing prediction intervals using Tube loss function

Innovation

• Uses Tube loss for probabilistic wind forecasting
• Incorporates LSTM, GRU, TCN in Tube framework
• Heuristic tuning for narrow prediction intervals