mlsanityData to 7 Oct 2026

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

arXiv 2501.08620 ↗First posted 15 Jan 2025cs.LGcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 20 of its 44 references that are in the map. First posted on arXiv 15 Jan 2025.

Builds on

20 of 44 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  5. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  6. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 396
  7. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 241
  8. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
  9. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  10. 2024RATT: A Thought Structure for Coherent and Correct LLM Reasoningcited by 39
  11. 2020Analysis of KNN Density Estimationcited by 28
  12. 2024A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Healthcited by 24
8 more
  1. 2022Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Datasetcited by 14
  2. 2023Deep Learning based Multi-Label Image Classification of Protest Activitiescited by 14
  3. 2024Robustness of Large Language Models Against Adversarial Attackscited by 12
  4. 2024MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformercited by 10
  5. 2023Towards a Pretrained Model for Restless Bandits via Multi-arm Generalizationcited by 9
  6. 2023Minimax Optimal Q Learning with Nearest Neighborscited by 8
  7. 2023Scalable Neural Network Kernelscited by 8
  8. 2024Enhancing Risk Assessment in Transformers with Loss-at-Risk Functionscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 12; 2026: 3.

15 papers.

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  1. 2026Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learningcited by 12
  2. 2025Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectivescited by 7
  3. 2025PhaseFormer: From Patches to Phases for Efficient and Effective Time Series Forecastingcited by 7
  4. 2025DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentationcited by 6
  5. 2025A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyondcited by 6
  6. 2025Bridging the Domain Gap in Equation Distillation with Reinforcement Feedbackcited by 4
  7. 2025A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planningcited by 4
  8. 2025Distribution Shift Aware Neural Tabular Learningcited by 4
  9. 2025CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentationcited by 3
  10. 2025Comparative Analysis of Large Language Models for Context-Aware Code Completion using SAFIM Frameworkcited by 3
  11. 2026EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networkscited by 2
  12. 2025Data-Efficient Symbolic Regression via Foundation Model Distillationcited by 1
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  1. 2025Segregation and Context Aggregation Network for Real-time Cloud Segmentationcited by 0
  2. 2025Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkerscited by 0
  3. 2026Multi-Agent Procedural Graph Extraction with Structural and Logical Refinementcited by 0