CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting

TL;DR

This paper introduces CT-PatchTST, a transformer-based model that improves long-term renewable energy forecasting by capturing temporal and inter-channel dependencies, aiding energy storage and grid management.

Contribution

The paper proposes a novel deep learning model, CT-PatchTST, that effectively captures temporal and inter-channel correlations for accurate long-term renewable energy forecasts.

Findings

Outperforms existing methods in accuracy and robustness.

Enables better energy storage planning and grid control.

Validated on real-world wind and solar datasets.

Abstract

Accurate forecasting of renewable energy generation is fundamental to enhancing the dynamic performance of modern power grids, especially under high renewable penetration. This paper presents Channel-Time Patch Time-Series Transformer (CT-PatchTST), a novel deep learning model designed to provide long-term, high-fidelity forecasts of wind and solar power. Unlike conventional time-series models, CT-PatchTST captures both temporal dependencies and inter-channel correlations-features that are critical for effective energy storage planning, control, and dispatch. Reliable forecasting enables proactive deployment of energy storage systems (ESSs), helping to mitigate uncertainties in renewable output, reduce system response time, and optimize storage operation based on location-specific flow and voltage conditions. Evaluated on real-world datasets from Denmark's offshore wind, onshore wind, and solar generation, CT-PatchTST outperforms existing methods in both accuracy and robustness. By enabling predictive, data-driven coordination of ESSs across integrated source-grid-load-storage systems, this work contributes to the design of more stable, responsive, and cost-efficient power networks.