Wind Speed Prediction using Deep Ensemble Learning with a Jet-like Architecture

• URL: http://arxiv.org/abs/2002.12592v2
• Date: Fri, 20 Mar 2020 16:41:12 GMT
• Title: Wind Speed Prediction using Deep Ensemble Learning with a Jet-like Architecture
• Authors: Aqsa Saeed Qureshi, Asifullah Khan, and Muhammad Waleed Khan
• Abstract summary: The design of wings, tail, and nose of a jet improves aerodynamics resulting in a smooth and controlled flight of the jet. The diverse feature spaces of the base-regressors are exploited using the jet-like ensemble architecture. The proposed DEL-Jet technique is evaluated for ten independent runs and shows that the deep and jet-like architecture helps in improving the robustness and generalization of the learning system.
• Score: 0.28675177318965034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The wind is one of the most increasingly used renewable energy resources. Accurate and reliable forecast of wind speed is necessary for efficient power production; however, it is not an easy task because it depends upon meteorological features of the surrounding region. Deep learning is extensively used these days for performing feature extraction. It has also been observed that the integration of several learning models, known as ensemble learning, generally gives better performance compared to a single model. The design of wings, tail, and nose of a jet improves the aerodynamics resulting in a smooth and controlled flight of the jet against the variations of the air currents. Inspired by the shape and working of a jet, a novel Deep Ensemble Learning using Jet-like Architecture (DEL-Jet) technique is proposed to enhance the diversity and robustness of a learning system against the variations in the input space. The diverse feature spaces of the base-regressors are exploited using the jet-like ensemble architecture. Two Convolutional Neural Networks (as jet wings) and one deep Auto-Encoder (as jet tail) are used to extract the diverse feature spaces from the input data. After that, nonlinear PCA (as jet main body) is employed to reduce the dimensionality of extracted feature space. Finally, both the reduced and the original feature spaces are exploited to train the meta-regressor (as jet nose) for forecasting the wind speed. The performance of the proposed DEL-Jet technique is evaluated for ten independent runs and shows that the deep and jet-like architecture helps in improving the robustness and generalization of the learning system.

Related papers

• D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition [60.84084172829169]
  Adapting large pre-trained image models to few-shot action recognition has proven to be an effective strategy for learning robust feature extractors. We present the Disentangled-and-Deformable Spatio-Temporal Adapter (D$2$ST-Adapter), which is a novel tuning framework well-suited for few-shot action recognition.
  arXiv  Detail & Related papers  (2023-12-03T15:40:10Z)
• Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information [0.0]
  We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. For the first time, we also introduce a generative model that goes beyond the kinematic features of jet constituents.
  arXiv  Detail & Related papers  (2023-11-30T19:00:02Z)
• Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis [74.00441177577295]
  Point cloud analysis faces computational system overhead, limiting its application on mobile or edge devices. This paper explores feature distillation for lightweight point cloud models. We propose bidirectional knowledge reconfiguration to distill informative contextual knowledge from the teacher to the student.
  arXiv  Detail & Related papers  (2023-10-08T11:32:50Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Physics Informed Shallow Machine Learning for Wind Speed Prediction [66.05661813632568]
  We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in Italy. We train supervised learning algorithms using the past history of wind to predict its value at a future time. We find that the optimal design as well as its performance vary with the location.
  arXiv  Detail & Related papers  (2022-04-01T14:55:10Z)
• Optimizing Airborne Wind Energy with Reinforcement Learning [0.0]
  Reinforcement Learning is a technique that learns to associate observations with profitable actions without requiring prior knowledge of the system. We show that in a simulated environment Reinforcement Learning finds an efficient way to control a kite so that it can tow a vehicle for long distances.
  arXiv  Detail & Related papers  (2022-03-27T10:28:16Z)
• Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural Network [0.0]
  Figuring out the right airfoil is a crucial step in the preliminary stage of any aerial vehicle design. This study compares different network architectures and training datasets in an attempt to gain insight as to how the network perceives the given airfoil geometries.
  arXiv  Detail & Related papers  (2021-09-24T19:07:19Z)
• Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions [13.00214468719929]
  Realtime model learning is challenging for complex dynamical systems, such as drones flying in variable wind conditions. We propose an online composite adaptation method that treats outputs from a deep neural network as a set of basis functions. We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories.
  arXiv  Detail & Related papers  (2021-03-02T18:43:59Z)

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.