Data is missing again -- Reconstruction of power generation data using $k$-Nearest Neighbors and spectral graph theory

• URL: http://arxiv.org/abs/2409.00300v1
• Date: Fri, 30 Aug 2024 23:58:28 GMT
• Title: Data is missing again -- Reconstruction of power generation data using $k$-Nearest Neighbors and spectral graph theory
• Authors: Amandine Pierrot, Pierre Pinson,
• Abstract summary: We propose an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing Nearest Neighbor imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only, or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information.

Related papers

• Learning to Optimise Wind Farms with Graph Transformers [6.519940858545459]
  The proposed model functions by encoding a wind farm into a fully-connected graph and processing the graph representation through a graph transformer. The graph transformer surrogate is shown to generalise well and is able to uncover latent structural patterns within the graph representation of wind farms.
  arXiv  Detail & Related papers  (2023-11-21T17:51:30Z)
• 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)
• End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning [7.850747042819504]
  This work proposes a surrogate model for the representation of wind turbine wakes based on a graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation.
  arXiv  Detail & Related papers  (2022-11-24T15:00:06Z)
• 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)
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [58.6106391721944]
  Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-27T12:46:52Z)
• 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)
• Measuring Wind Turbine Health Using Drifting Concepts [55.87342698167776]
  We propose two new approaches for the analysis of wind turbine health. The first method aims at evaluating the decrease or increase in relatively high and low power production. The second method evaluates the overall drift of the extracted concepts.
  arXiv  Detail & Related papers  (2021-12-09T14:04:55Z)
• Multi Scale Graph Wavenet for Wind Speed Forecasting [0.0]
  We propose a novel deep learning architecture, Multi Scale Graph Wavenet for wind speed forecasting. It is based on a graph convolutional neural network and captures both spatial and temporal relationships in time series weather data. We conducted experiments on real wind speed data measured at different cities in Denmark and compared our results with the state-of-the-art baseline models.
  arXiv  Detail & Related papers  (2021-09-30T16:18:30Z)
• Deep Spatio-Temporal Wind Power Forecasting [4.219722822139438]
  We develop a deep learning approach based on encoder-decoder structure. Our model forecasts wind power generated by a wind turbine using its spatial location relative to other turbines and historical wind speed data.
  arXiv  Detail & Related papers  (2021-09-29T16:26:10Z)
• Wind Power Projection using Weather Forecasts by Novel Deep Neural Networks [0.0]
  Using optimized machine learning algorithms, it is possible to find obscured patterns in the observations and obtain meaningful data. The paper explores the use of both parametric and the non-parametric models for calculating wind power prediction using power curves.
  arXiv  Detail & Related papers  (2021-08-22T17:46:36Z)
• Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
  Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
  arXiv  Detail & Related papers  (2020-04-11T19:49:09Z)

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.