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

• URL: http://arxiv.org/abs/2501.08620v3
• Date: Thu, 28 Aug 2025 04:30:21 GMT
• Title: CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting
• Authors: Kuan Lu, Menghao Huo, Yuxiao Li, Qiang Zhu, Zhenrui Chen,
• Abstract summary: This paper presents a novel deep learning model designed to provide long-term, high-fidelity forecasts of wind and solar power.<n>CT-PatchTST captures both temporal dependencies and inter-channel correlations-features that are critical for effective energy storage planning, control, and dispatch.<n>This work contributes to the design of more stable, responsive, and cost-efficient power networks.
• Score: 4.70088249301921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Ultra-short-term solar power forecasting by deep learning and data reconstruction [60.200987006598524]
  We propose a deep-learning based ultra-short-term solar power prediction with data reconstruction.<n>We employ deep-learning models to capture long- and short-term dependencies towards the target prediction period.
  arXiv  Detail & Related papers  (2025-09-21T14:22:35Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• Fast-Powerformer: A Memory-Efficient Transformer for Accurate Mid-Term Wind Power Forecasting [4.2707347040807475]
  Wind power forecasting plays a crucial role in enhancing the security, stability, and economic operation of power grids. Due to the highity of meteorological factors (e.g., wind speed) and significant fluctuations in wind power output, mid-term wind power forecasting faces a dual challenge of maintaining high accuracy and computational efficiency. This paper proposes an efficient and lightweight mid-term wind power forecasting model, termed Fast-Powerformer.
  arXiv  Detail & Related papers  (2025-04-15T07:09:54Z)
• Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting [50.298817606660826]
  We introduce Powerformer, a novel Transformer variant that replaces noncausal attention weights with causal weights that are reweighted according to a smooth heavy-tailed decay.<n>Our empirical results demonstrate that Powerformer achieves state-of-the-art accuracy on public time-series benchmarks.<n>Our analyses show that the model's locality bias is amplified during training, demonstrating an interplay between time-series data and power-law-based attention.
  arXiv  Detail & Related papers  (2025-02-10T04:42:11Z)
• Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions [16.705621552594643]
  Existing models often struggle with capturing the complex relationships between target variables and covariates.<n>We propose a novel model architecture that leverages the iTransformer for feature extraction from target variables.<n>A cross-attention mechanism is integrated to fuse the outputs of both models, followed by a Kolmogorov-Arnold network mapping.<n>Results demonstrate that the proposed model effectively capture seasonal variations in PV power generation and improve forecasting accuracy.
  arXiv  Detail & Related papers  (2024-12-03T09:16:13Z)
• Hiformer: Hybrid Frequency Feature Enhancement Inverted Transformer for Long-Term Wind Power Prediction [6.936415534357298]
  We propose a novel approach called Hybrid Frequency Feature Enhancement Inverted Transformer (Hiformer) Hiformer integrates signal decomposition technology with weather feature extraction technique to enhance the modeling of correlations between meteorological conditions and wind power generation. Compared to the state-of-the-art methods, Hiformer: (i) can improve the prediction accuracy by up to 52.5%; and (ii) can reduce computational time by up to 68.5%.
  arXiv  Detail & Related papers  (2024-10-17T08:00:36Z)
• TCNFormer: Temporal Convolutional Network Former for Short-Term Wind Speed Forecasting [9.47727784069628]
  This study proposes the Temporal ConTemporalal Former (TCNFormer) for short-term (12-hour) wind speed forecasting. The findings indicate that the TCNFormer outperforms state-of-the-art models in prediction accuracy.
  arXiv  Detail & Related papers  (2024-08-27T15:35:42Z)
• Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data [4.048814984274799]
  This paper proposes a novel methodology for deterministic wind and solar energy generation forecasting for multiple generation sites.<n>The method employs a U-shaped Temporal Convolutional Auto-Encoder architecture for temporal processing of weather-related and energy-related time-series.<n>The conducted experimental evaluation on a day-ahead solar and wind energy forecasting scenario on five datasets demonstrated that the proposed method achieves top results.
  arXiv  Detail & Related papers  (2024-04-26T09:30:55Z)
• TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework [58.474610046294856]
  Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. This paper introduces an integrated framework that leverages the capabilities of the Transformer model-based neural networks and deep reinforcement learning (DRL) algorithms to optimize system maintenance actions.
  arXiv  Detail & Related papers  (2023-09-29T02:27:54Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Long-Term Hourly Scenario Generation for Correlated Wind and Solar Power combining Variational Autoencoders with Radial Basis Function Kernels [0.0]
  We propose an innovative method for generating long-term hourly scenarios for wind and solar power generation. By incorporating the Radial Basis Function (RBF) kernel in our artificial neural network architecture, we aim to obtain a latent space with improved regularization properties.
  arXiv  Detail & Related papers  (2023-06-27T14:02:10Z)
• Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer [112.12271800369741]
  Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages. Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system operations. Existing methods are mainly designed for short-term predictions and lack effective spatial-temporal feature augmentation.
  arXiv  Detail & Related papers  (2023-05-30T04:03:15Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.