The transition from conventional methods of energy production to renewable energy production necessitates better prediction models of the upcoming supply of renewable energy. In wind power production, error in forecasting production is impossible to negate owing to the intermittence of wind. For successful power grid integration, it is crucial to understand the uncertainties that arise in predicting wind power production and use this information to build an accurate and reliable forecast. This can be achieved by observing the fluctuations in wind power production with changes in different parameters such as wind speed, temperature, and wind direction, and deriving functional dependencies for the same. Using optimized machine learning algorithms, it is possible to find obscured patterns in the observations and obtain meaningful data, which can then be used to accurately predict wind power requirements . Utilizing the required data provided by the Gamesa's wind farm at Bableshwar, the paper explores the use of both parametric and the non-parametric models for calculating wind power prediction using power curves. The obtained results are subject to comparison to better understand the accuracy of the utilized models and to determine the most suitable model for predicting wind power production based on the given data set.
ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology（香港科技大学）
In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by downloading $43$ years of hourly global weather data from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep neural networks with about $256$ million parameters in total. The spatial resolution of forecast is $0.25^\circ\times0.25^\circ$, comparable to the ECMWF Integrated Forecast Systems (IFS). More importantly, for the first time, an AI-based method outperforms state-of-the-art numerical weather …
FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting
Shanghai AI Laboratory（上海人工智能实验室）；Hong Kong University of Science and Technology（香港科技大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；National Meteorological Information Center of CMA（中国气象局国家气象信息中心）；National Meteorological Center of CMA（中国气象局国家气象中心）
EWMoE: An effective model for global weather forecasting with mixture-of-experts
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Artificial Intelligence Research Institute（四川人工智能研究院）；Institute of Plateau Meteorology, China Meteorological Administration（中国气象局高原气象研究所）
Deep learning-based method for weather forecasting: A case study in Itoshima
Joint Graduate School of Mathematics for Innovation, Kyushu University（九州大学创新数学联合研究生院）；International Institute for Carbon-Neutral Energy Research, Kyushu University（九州大学国际碳中和能源研究所）；Institute of Tropical Agriculture, Kyushu University（九州大学热带农业研究所）；Faculty of Agriculture, Kyushu University（九州大学农学院）
Stockholm University（斯德哥尔摩大学）；Uppsala University（乌普萨拉大学）
Ensemble weather forecasts enable a measure of uncertainty to be attached to each forecast, by computing the ensemble's spread. However, generating an ensemble with a good spread-error relationship is far from trivial, and a wide range of approaches to achieve this have been explored -- chiefly in the context of numerical weather prediction models. Here, we aim to transform a deterministic neural network weather forecasting system into an ensemble forecasting system. We test four methods to generate the ensemble: random initial perturbations, retraining of the neural network, use of random dro…
FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）；National Meteorological Information Center, China Meteorological Administration（中国气象局国家气象信息中心）
Technical University of Munich（慕尼黑工业大学）；ClimateAi, Inc（ClimateAi公司）
WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined evaluation metrics and a number of baseline models. WeatherBench Probability extends this to probabilistic forecasting by adding a set of established probabilistic verification metrics (continuous ranked probability score, spread-skill ratio and rank histograms) and a state-of-the-art operational baseline using the ECWMF IFS ensemble forecast. In addition, we test three different probabilistic machine learning methods -- Monte Carl…
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
Nanjing University of Information Science and Technology（南京信息工程大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hongkong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）
University of Cambridge（剑桥大学）；Vector Institute（矢量研究所）；University of Toronto（多伦多大学）；Microsoft Research（微软研究院）；British Antarctic Survey（英国南极调查局）；The Alan Turing Institute（阿兰·图灵研究所）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；Google Deepmind（谷歌DeepMind）
Not only can discovering patterns and insights from atmospheric data enable more accurate weather predictions, but it may also provide valuable information to help tackle climate change. Weather4cast is an open competition that aims to evaluate machine learning algorithms' capability to predict future atmospheric states. Here, we describe our third-place solution to Weather4cast. We present a novel Variational U-Net that combines a Variational Autoencoder's ability to consider the probabilistic nature of data with a U-Net's ability to recover fine-grained details. This solution is an evolution…
Weather forecast plays an essential role in multiple aspects of the daily life of human beings. Currently, physics based numerical weather prediction is used to predict the weather and requires enormous amount of computational resources. In recent years, deep learning based models have seen wide success in many weather-prediction related tasks. In this paper we describe our experiments for the Weather4cast 2021 Challenge, where 8 hours of spatio-temporal weather data is predicted based on an initial one hour of spatio-temporal data. We focus on SmaAt-UNet, an efficient U-Net based autoencoder.…
Traditionally, weather predictions are performed with the help of large complex models of physics, which utilize different atmospheric conditions over a long period of time. These conditions are often unstable because of perturbations of the weather system, causing the models to provide inaccurate forecasts. The models are generally run on hundreds of nodes in a large High Performance Computing (HPC) environment which consumes a large amount of energy. In this paper, we present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine l…
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution limits. We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy. FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Bo…
