Short-term wind speed forecasting model based on an attention-gated recurrent neural network and error correction strategy

• URL: http://arxiv.org/abs/2404.11422v2
• Date: Mon, 22 Apr 2024 11:21:31 GMT
• Title: Short-term wind speed forecasting model based on an attention-gated recurrent neural network and error correction strategy
• Abstract summary: This dissertation raises one short-term wind speed forecast pattern on the foundation of attention with an improved gated recurrent neural network (AtGRU) The proposed SSA-AtGRU-VMD-GRU model outperforms the compared models in three case studies on Woodburn, St. Thomas, and Santa Cruz.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate wind speed series forecast is very pivotal to security of grid dispatching and the application of wind power. Nevertheless, on account of their nonlinear and non-stationary nature, their short-term forecast is extremely challenging. Therefore, this dissertation raises one short-term wind speed forecast pattern on the foundation of attention with an improved gated recurrent neural network (AtGRU) and a tactic of error correction. That model uses the AtGRU model as the preliminary predictor and the GRU model as the error corrector. At the beginning, SSA (singular spectrum analysis) is employed in previous wind speed series for lessening the noise. Subsequently, historical wind speed series is going to be used for the predictor training. During this process, the prediction can have certain errors. The sequence of these errors processed by variational modal decomposition (VMD) is used to train the corrector of error. The eventual forecast consequence is just the sum of predictor forecast and error corrector. The proposed SSA-AtGRU-VMD-GRU model outperforms the compared models in three case studies on Woodburn, St. Thomas, and Santa Cruz. It is indicated that the model evidently enhances the correction of the wind speed forecast.

Related papers

• A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors [0.0]
  We develop a hybrid LSTM-Vision Transformer (LSTM-ViT) framework that combines temporal sequence learning from surface observations with atmospheric profiles from the New York State Mesonet profiler network.<n>Our research offers forecasters enhanced guidance regarding model bias and forecast confidence.
  arXiv  Detail & Related papers  (2026-06-17T12:51:46Z)
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• Forecast collapse of transformer-based models under squared loss in financial time series [0.0]
  We study trajectory forecasting under squared loss for time series with weak conditional structure.<n>We emphasize regimes in which the conditional expectation of future trajectories is effectively degenerate.<n>Increased model expressivity does not improve predictive accuracy but instead introduces spurious trajectory fluctuations around the optimal predictor.
  arXiv  Detail & Related papers  (2026-03-31T09:06:52Z)
• Target Concept Tuning Improves Extreme Weather Forecasting [51.43578341469291]
  Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons.<n>Existing fine-tuning methods face a trade-off between overlooking these extreme events and overfitting them at the expense of overall performance.<n>We propose TaCT, an interpretable concept-gated fine-tuning framework that solves the aforementioned issue by selective model improvement.
  arXiv  Detail & Related papers  (2026-03-17T15:34:05Z)
• An Optimization Method for Autoregressive Time Series Forecasting [4.467218412104476]
  Current time-series forecasting models are primarily based on transformer-style neural networks.<n>We propose a novel training method for time-series forecasting that enforces two key properties.<n>Our method achieves an MSE reduction of more than 10% compared to iTransformer.
  arXiv  Detail & Related papers  (2026-02-02T16:28:00Z)
• Neural CDEs as Correctors for Learned Time Series Models [0.0]
  We propose a Predictor-Corrector mechanism where the Predictor is any learned time-series model and the Corrector is a neural controlled differential equation.<n>The proposed Corrector works with irregularly sampled time series and continuous- and discrete-time Predictors.<n>We evaluate our Corrector with diverse Predictors on synthetic, physics simulation, and real-world forecasting datasets.
  arXiv  Detail & Related papers  (2025-12-13T01:17:05Z)
• SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization [62.958457694151384]
  We introduce preference optimization into precipitation nowcasting for the first time, motivated by the success of reinforcement learning from human feedback in large language models.<n>In the first stage, the framework focuses on reducing FAR, training the model to effectively suppress false alarms.
  arXiv  Detail & Related papers  (2025-10-22T16:11:22Z)
• Accuracy Law for the Future of Deep Time Series Forecasting [65.46625911002202]
  Time series forecasting inherently faces a non-zero error lower bound due to its partially observable and uncertain nature.<n>This paper focuses on a fundamental question: how to estimate the performance upper bound of deep time series forecasting.<n>Based on rigorous statistical tests of over 2,800 newly trained deep forecasters, we discover a significant exponential relationship between the minimum forecasting error of deep models and the complexity of window-wise series patterns.
  arXiv  Detail & Related papers  (2025-10-03T05:18:47Z)
• Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback [41.94295877935867]
  In automated driving, predicting trajectories of surrounding vehicles supports reasoning about scene dynamics and enables safe planning for the ego vehicle. Existing models handle predictions as an instantaneous task of forecasting future trajectories based on observed information. We propose a novel retrospection technique to correct its errors during inference and will repeat them.
  arXiv  Detail & Related papers  (2025-04-18T16:35:12Z)
• Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization [27.047129496488292]
  Predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI)
  arXiv  Detail & Related papers  (2024-06-29T05:36:04Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Scaling transformer neural networks for skillful and reliable medium-range weather forecasting [23.249955524044392]
  We introduce Stormer, a state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days.
  arXiv  Detail & Related papers  (2023-12-06T19:46:06Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Ultra-short-term multi-step wind speed prediction for wind farms based on adaptive noise reduction technology and temporal convolutional network [0.0]
  This study proposes a new wind speed prediction model based on data noise reduction technology, temporal convolutional network (TCN), and gated recurrent unit (GRU) The proposed model was validated on three wind farms in Shandong Province.
  arXiv  Detail & Related papers  (2023-11-27T03:53:19Z)
• Towards Long-Term predictions of Turbulence using Neural Operators [68.8204255655161]
  It aims to develop reduced-order/surrogate models for turbulent flow simulations using Machine Learning. Different model structures are analyzed, with U-NET structures performing better than the standard FNO in accuracy and stability.
  arXiv  Detail & Related papers  (2023-07-25T14:09:53Z)
• Debiased Fine-Tuning for Vision-language Models by Prompt Regularization [50.41984119504716]
  We present a new paradigm for fine-tuning large-scale vision pre-trained models on downstream task, dubbed Prompt Regularization (ProReg) ProReg uses the prediction by prompting the pretrained model to regularize the fine-tuning. We show the consistently strong performance of ProReg compared with conventional fine-tuning, zero-shot prompt, prompt tuning, and other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-01-29T11:53:55Z)
• Spatiotemporal Weather Data Predictions with Shortcut Recurrent-Convolutional Networks: A Solution for the Weather4cast challenge [0.0]
  This paper presents the neural network model that was used by the author in the Weather4cast 2021 Challenge Stage 1. The objective was to predict the time evolution of satellite-based weather data images. The network is based on an encoder-forecaster architecture making use of gated recurrent units (GRU), residual blocks and a contracting/expanding architecture with shortcuts similar to U-Net.
  arXiv  Detail & Related papers  (2021-11-03T10:36:47Z)
• Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach [0.0]
  Many-to-Many RNN storm trajectory prediction models presented here are significantly faster than ensemble models used by the NHC. A detailed analysis of model forecasting error shows that Many-To-One prediction models are less accurate than Many-To-Many models owing to compounded error accumulation.
  arXiv  Detail & Related papers  (2021-10-30T00:31:48Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Understanding and Mitigating the Tradeoff Between Robustness and Accuracy [88.51943635427709]
  Adversarial training augments the training set with perturbations to improve the robust error. We show that the standard error could increase even when the augmented perturbations have noiseless observations from the optimal linear predictor.
  arXiv  Detail & Related papers  (2020-02-25T08:03:01Z)

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.