Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind
نویسندگان
چکیده
منابع مشابه
A Neural Network for Damaging Wind Prediction
A neural network is developed to diagnose which circulations detected by the National Severe Storms Laboratory's (NSSL) Mesocyclone Detection Algorithm (MDA) yield damaging wind. In particular, 23 variables characterizing the circulations are selected to be used as the input nodes of a feed-forward, supervised neural network. The outputs of the network represent the existence/nonexistence of da...
متن کاملWind Power Prediction with Machine Learning
Better predictionmodels for the upcoming supply of renewable energy are important to decrease the need of controlling energy provided by conventional power plants. Especially for successful power grid integration of the highly volatile wind power production, a reliable forecast is crucial. In this chapter, we focus on shortterm wind power prediction and employ data from the National Renewable E...
متن کاملWind Power Prediction with Machine Learning Ensembles
For a sustainable integration of wind power into the electricity grid, precise and robust predictions are required. With increasing installed capacity and changing energy markets, there is a growing demand for short-term predictions. Machine learning methods can be used as a purely data-driven, spatio-temporal prediction model that yields better results than traditional physical models based on...
متن کاملA machine-learning algorithm for wind gust prediction
Physical damage to property and crops caused by unanticipated wind gusts is a well understood phenomenon. Predicting its occurrence continues to be a challenge for meteorologists and climatologists. Various approaches to gust occurrence model building have been proposed. The very nature of the event is problematic because of its brief duration following a rapid change of state in wind velocity ...
متن کاملReal-time Scheduling of a Flexible Manufacturing System using a Two-phase Machine Learning Algorithm
The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Weather and Forecasting
سال: 2017
ISSN: 0882-8156,1520-0434
DOI: 10.1175/waf-d-17-0038.1