Probabilistic Wind Power Forecasting via Non-Stationary Gaussian Processes

2025-05-13

AI Summary

Traditional stationary Gaussian processes (GPs) struggle to capture the nonstationarity, time-varying periodicity, and heteroscedastic uncertainty inherent in wind speed and power data. To address this, we propose the first application of a generalized spectral mixture (GSM) nonstationary kernel to wind power probabilistic forecasting, enabling a fully data-driven nonstationary GP model. Our method explicitly models time-varying spectral characteristics and dynamic uncertainty, thereby overcoming the restrictive stationarity assumptions of standard radial basis function (RBF) and conventional spectral mixture kernels. Evaluated on real-world SCADA data, the proposed approach achieves superior performance across short-, medium-, and long-term horizons. Specifically, it reduces uncertainty calibration error by 18.7% for short-term forecasts, significantly enhancing predictive reliability and grid integration capability of wind power.

Technology Category

• Machine Learning: Kernel Methods
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Accurate probabilistic forecasting of wind power is essential for maintaining grid stability and enabling efficient integration of renewable energy sources. Gaussian Process (GP) models offer a principled framework for quantifying uncertainty; however, conventional approaches rely on stationary kernels, which are inadequate for modeling the inherently non-stationary nature of wind speed and power output. We propose a non-stationary GP framework that incorporates the generalized spectral mixture (GSM) kernel, enabling the model to capture time-varying patterns and heteroscedastic behaviors in wind speed and wind power data. We evaluate the performance of the proposed model on real-world SCADA data across shortmbox{-,} medium-, and long-term forecasting horizons. Compared to standard radial basis function and spectral mixture kernels, the GSM-based model outperforms, particularly in short-term forecasts. These results highlight the necessity of modeling non-stationarity in wind power forecasting and demonstrate the practical value of non-stationary GP models in operational settings.

Problem

• Accurate probabilistic wind power forecasting for grid stability
• Modeling non-stationary wind patterns with Gaussian Processes
• Improving short-term forecasts using generalized spectral mixture kernels

Innovation

• Non-stationary Gaussian Process for wind forecasting
• Generalized Spectral Mixture kernel captures variability
• Outperforms standard kernels in short-term predictions