EPT-1.5 Technical Report

• URL: http://arxiv.org/abs/2410.15076v2
• Date: Sun, 03 Nov 2024 18:11:28 GMT
• Title: EPT-1.5 Technical Report
• Abstract summary: EPT-1.5 shows remarkable performance in predicting energy-relevant variables, particularly 10m & 100m wind speed and solar radiation. Especially in wind prediction, it outperforms existing AI weather models like GraphCast, FuXi, and Pangu-Weather.
• Score: 31.466883373381165
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We announce the release of EPT-1.5, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI earth system models. EPT-1.5 demonstrates substantial improvements over its predecessor, EPT-1. Built specifically for the European energy industry, EPT-1.5 shows remarkable performance in predicting energy-relevant variables, particularly 10m & 100m wind speed and solar radiation. Especially in wind prediction, it outperforms existing AI weather models like GraphCast, FuXi, and Pangu-Weather, as well as the leading numerical weather model, IFS HRES by the European Centre for Medium-Range Weather Forecasts (ECMWF), setting a new state of the art.

Related papers

• Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry [2.083477641291698]
  We present Aries, a SwinTransformer-based machine-learned weather prediction model developed at InCommodities.<n>Aries is trained on ERA5 reanalysis data at 0.25degree resolution, predicting 74 prognostic and 11 diagnostic atmospheric variables.<n>On 10-metre wind speed, Aries outperforms both ECMWF HRES and AIFS in terms of RMSE for lead times up to four days, while on 2-metre temperature it achieves RMSE on par with AIFS operational.
  arXiv  Detail & Related papers  (2026-09-09T11:24:00Z)
• Do AI weather models miss extremes? [3.3270769893417707]
  First-generation AI weather models are often reported to underperform at extremes.<n>We verify eleven physical and AI forecast systems against European synoptic, solar, and rain-gauge stations over ten months.<n>Among these systems, AI models do not show a uniform relative-skill deficit in the tails.
  arXiv  Detail & Related papers  (2026-07-31T14:18:43Z)
• A Weather Foundation Model for the Power Grid [4.060631090375762]
  We fine-tune Silurian AI's WFM, Generative Forecasting Transformer (GFT)<n>It delivers hyper-local, asset-level forecasts for five grid-critical variables.<n>It attains an average precision score of 0.72 for day-ahead rime-ice detection.
  arXiv  Detail & Related papers  (2025-09-28T08:05:46Z)
• EPT-2 Technical Report [30.496955543691026]
  EPT-2 is the latest iteration in our Earth Physics Transformer family of foundation AI models for Earth system forecasting.<n>It consistently outperforms leading AI weather models such as Microsoft Aurora, as well as the operational numerical forecast system IFS HRES.<n>In parallel, we introduce a perturbation-based ensemble model of EPT-2 for probabilistic forecasting, called EPT-2e.
  arXiv  Detail & Related papers  (2025-07-13T16:32:13Z)
• ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting [11.561776315905357]
  We propose a methodology to leverage deterministic weather models in the design of probabilistic weather models. Our work also aims to democratize the use of deterministic and generative machine learning models in weather forecasting research.
  arXiv  Detail & Related papers  (2024-12-17T14:54:30Z)
• 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)
• FuXi-2.0: Advancing machine learning weather forecasting model for practical applications [11.50902060124504]
  FuXi-2.0 is an advanced machine learning model that delivers 1-hourly global weather forecasts and includes a comprehensive set of meteorological variables. FuXi-2.0 consistently outperforms ECMWF HRES in forecasting key meteorological variables relevant to wind and solar energy, aviation, and marine shipping sectors. FuXi-2.0 also integrates both atmospheric and oceanic components, representing a significant step forward in the development of coupled atmospheric-ocean models.
  arXiv  Detail & Related papers  (2024-09-11T11:21:00Z)
• Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models [0.08271752505511926]
  Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. Recently released suite of AI-based weather models produces medium-range forecasts within seconds. We assess the forecast skill of three top-performing AI-models for convective parameters against reanalysis and ECMWF's operational numerical weather prediction model IFS.
  arXiv  Detail & Related papers  (2024-06-13T07:46:03Z)
• Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal [0.15361702135159847]
  The model was evaluated using a leave one year out cross-validation and compared with the climatological benchmark. Results show that the transformer model outperforms the benchmark model for one month forecasting horizon, after which the climatological benchmark is better. Overall, the tested Transformer model is a valid method for FAPAR forecasting, especially when combined with weather data and used for short-term predictions.
  arXiv  Detail & Related papers  (2024-02-08T11:00:51Z)
• FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead [93.67314652898547]
  We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI) FengWu is able to accurately reproduce the atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25deg latitude-longitude resolution. The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead.
  arXiv  Detail & Related papers  (2023-04-06T09:16:39Z)
• A Deep Learning Method for Real-time Bias Correction of Wind Field Forecasts in the Western North Pacific [24.287588853356972]
  Real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021. Wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively.
  arXiv  Detail & Related papers  (2022-12-29T02:58:12Z)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
  Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
  arXiv  Detail & Related papers  (2022-05-20T22:13:25Z)
• Calibration of wind speed ensemble forecasts for power generation [0.0]
  In the last decades wind power became the second largest energy source in the EU covering 16% of its electricity demand. Due to its volatility, accurate short range wind power predictions are required for successful integration of wind energy into the electrical grid. We show that compared with the raw ensemble, post-processing always improves the calibration of probabilistic and accuracy of point forecasts.
  arXiv  Detail & Related papers  (2021-04-30T11:18:03Z)
• 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.