KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution

• URL: http://arxiv.org/abs/2208.08952v1
• Date: Thu, 18 Aug 2022 16:46:50 GMT
• Title: KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution
• Authors: Fangquan Lin, Wei Jiang, Hanwei Zhang, Cheng Yang
• Abstract summary: This paper describes the solution of Team 88VIP, which mainly comprises two types of models. The proposed solution achieves an overall online score of -45.213 in Phase 3.
• Score: 12.78127754761155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors. The evaluation metrics contain RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly comprises two types of models: a gradient boosting decision tree to memorize the basic data patterns and a recurrent neural network to capture the deep and latent probabilistic transitions. Ensembling these models contributes to tackle the fluctuation of wind power, and training submodels targets on the distinguished properties in heterogeneous timescales of forecasting, from minutes to days. In addition, feature engineering, imputation techniques and the design of offline evaluation are also described in details. The proposed solution achieves an overall online score of -45.213 in Phase 3.

Related papers

• On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching [0.6001424997506751]
  Day-ahead forecasts are necessary to communicate Wind Power (WP) availability for redispatch planning. The irregular interventions into the WP generation capabilities due to redispatch shutdowns pose challenges in the design and operation of WP forecasting models. This paper analyzes state-of-the-art forecasting methods on data sets with both regular and irregular shutdowns.
  arXiv  Detail & Related papers  (2024-11-30T10:30:11Z)
• FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere [53.22497376154084]
  We propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO) Our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential
  arXiv  Detail & Related papers  (2024-11-15T13:44:37Z)
• Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers [29.33938664834226]
  Subseasonal forecasting is pivotal for agriculture, water resource management, and early warning of disasters. Recent advances in machine learning have revolutionized weather forecasting by achieving competitive predictive skills to numerical models. However, training such foundation models requires thousands of GPU days, which causes substantial carbon emissions.
  arXiv  Detail & Related papers  (2024-01-31T14:27:35Z)
• Asset Bundling for Wind Power Forecasting [15.393565192962482]
  This work proposes a Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling, machine learning, and forecast reconciliation techniques. The BPR framework first learns an intermediate hierarchy level (the bundles), then predicts wind power at the asset, bundle, and fleet level, and finally reconciles all forecasts to ensure consistency.
  arXiv  Detail & Related papers  (2023-09-28T14:56:34Z)
• 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)
• Deep learning for spatio-temporal forecasting -- application to solar energy [12.5097469793837]
  This thesis tackles the subject of principled-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images.
  arXiv  Detail & Related papers  (2022-05-07T06:42:48Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Random vector functional link neural network based ensemble deep learning for short-term load forecasting [14.184042046855884]
  This paper proposes a novel ensemble deep Random Functional Link (edRVFL) network for electricity load forecasting. The hidden layers are stacked to enforce deep representation learning. The model generates the forecasts by ensembling the outputs of each layer.
  arXiv  Detail & Related papers  (2021-07-30T01:20:48Z)
• Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track [78.64815984927425]
  The goal of weakly-supervised temporal action localization is to temporally locate and classify action of interest in untrimmed videos. We adopt the two-stream consensus network (TSCN) as the main framework in this challenge. Our solution ranked 2rd in this challenge, and we hope our method can serve as a baseline for future academic research.
  arXiv  Detail & Related papers  (2021-06-21T03:36:36Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

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.