Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features.

ABSTRACT: Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influ

SUBMITTER: Wang M 

PROVIDER: S-EPMC11527990 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature