Multi-resolution spatio-temporal prediction with application to wind power generation

TL;DR

This paper introduces a multi-resolution spatio-temporal Gaussian process model for wind speed prediction that effectively quantifies uncertainty and leverages diverse data sources to improve accuracy for renewable energy integration.

Contribution

The paper presents a novel multi-resolution Gaussian process framework that enhances wind speed prediction accuracy and uncertainty quantification by integrating multiple data sources with varying resolutions.

Findings

Improved wind speed prediction accuracy over existing methods.

Effective uncertainty quantification for wind energy forecasts.

Enhanced data integration from multiple sources improves model robustness.

Abstract

Wind energy is becoming an increasingly crucial component of a sustainable grid, but its inherent variability and limited predictability present challenges for grid operators. The energy sector needs novel forecasting techniques that can precisely predict the generation of renewable power and offer precise quantification of prediction uncertainty. This will facilitate well-informed decision-making by operators who wish to integrate renewable energy into the power grid. This paper presents a novel approach to wind speed prediction with uncertainty quantification using a multi-resolution spatio-temporal Gaussian process. By leveraging information from multiple sources of predictions with varying accuracies and uncertainties, the joint framework provides a more accurate and robust prediction of wind speed while measuring the uncertainty in these predictions. We assess the effectiveness of our proposed framework using real-world wind data obtained from the Midwest region of the United States. Our results demonstrate that the framework enables predictors with varying data resolutions to learn from each other, leading to an enhancement in overall predictive performance. The proposed framework shows a superior performance compared to other state-of-the-art methods. The goal of this research is to improve grid operation and management by aiding system operators and policymakers in making better-informed decisions related to energy demand management, energy storage system deployment, and energy supply scheduling. This results in potentially further integration of renewable energy sources into the existing power systems.