A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction

• URL: http://arxiv.org/abs/2203.15298v1
• Date: Tue, 29 Mar 2022 07:31:16 GMT
• Title: A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction
• Authors: G.V. Drisya, K. Satheesh Kumar
• Abstract summary: The wind speed time series are split into various frequency components using wavelet decomposition technique. Since the components associated with the high-frequency range shows nature, we modelled them with autoregressive (AR) method. The results of the hybrid method show a promising improvement in accuracy of wind speed prediction compared to that of stand-alone AR or SVM model.
• Score: 0.9137554315375922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wind speed modelling and prediction has been gaining importance because of its significant roles in various stages of wind energy management. In this paper, we propose a hybrid model, based on wavelet transform to improve the accuracy of the short-term forecast. The wind speed time series are split into various frequency components using wavelet decomposition technique, and each frequency components are modelled separately. Since the components associated with the high- frequency range shows stochastic nature, we modelled them with autoregressive (AR) method and rest of low-frequency components modelled with support vector machine (SVM). The results of the hybrid method show a promising improvement in accuracy of wind speed prediction compared to that of stand-alone AR or SVM model.

Related papers

• Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction [1.1270209626877075]
  The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations. Traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling.
  arXiv  Detail & Related papers  (2025-02-11T08:16:48Z)
• Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia [0.0]
  Wind power generation is always accompanied by uncertainty due to the wind speed's volatility. Wind speed forecasting (WSF) is essential for power grids' dispatch, stability, and controllability. This study proposes a novel WSF framework for stationary data based on a hybrid decomposition method.
  arXiv  Detail & Related papers  (2024-12-17T22:04:46Z)
• Short-term Wind Speed Forecasting for Power Integration in Smart Grids based on Hybrid LSSVM-SVMD Method [0.0]
  Wind energy has become one of the most widely exploited renewable energy resources. The successful integration of wind power into the grid system is contingent upon accurate wind speed forecasting models. In this paper, a hybrid machine learning approach is developed for predicting short-term wind speed.
  arXiv  Detail & Related papers  (2024-08-30T10:35:59Z)
• Spatial Annealing for Efficient Few-shot Neural Rendering [73.49548565633123]
  We introduce an accurate and efficient few-shot neural rendering method named textbfSpatial textbfAnnealing regularized textbfNeRF (textbfSANeRF) By adding merely one line of code, SANeRF delivers superior rendering quality and much faster reconstruction speed compared to current few-shot neural rendering methods.
  arXiv  Detail & Related papers  (2024-06-12T02:48:52Z)
• A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization [54.113083217869516]
  In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
  arXiv  Detail & Related papers  (2023-12-24T15:37:47Z)
• Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation [69.1351513309953]
  We show that accurately simulating the low-frequency components of Room Impulse Responses (RIRs) is important to achieving good dereverberation. We demonstrate that speech dereverberation models trained on hybrid synthetic RIRs outperform models trained on RIRs generated by prior geometric ray tracing methods.
  arXiv  Detail & Related papers  (2022-12-10T20:15:23Z)
• Diffusion Probabilistic Model Made Slim [128.2227518929644]
  We introduce a customized design for slim diffusion probabilistic models (DPM) for light-weight image synthesis. We achieve 8-18x computational complexity reduction as compared to the latent diffusion models on a series of conditional and unconditional image generation tasks.
  arXiv  Detail & Related papers  (2022-11-27T16:27:28Z)
• Multi-Step Short-Term Wind Speed Prediction with Rank Pooling and Fast Fourier Transformation [0.0]
  Short-term wind speed prediction is essential for economical wind power utilization. The real-world wind speed data is typically intermittent and fluctuating, presenting great challenges to existing shallow models. We present a novel deep hybrid model for multi-step wind speed prediction, namely LR-FFT-RP-MLP/LSTM.
  arXiv  Detail & Related papers  (2022-11-23T14:02:52Z)
• FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization [73.41395947275473]
  We propose a novel frequency-aware architecture, in which the domain-specific features are filtered out in the transformed frequency domain. Experiments on three benchmarks demonstrate significant performance, outperforming the state-of-the-art methods by a margin of 3%, 4% and 9%, respectively.
  arXiv  Detail & Related papers  (2022-03-24T07:26:29Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)
• Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm [1.032905038435237]
  Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy.
  arXiv  Detail & Related papers  (2020-02-14T19:29:33Z)

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.