Tree-Based Forecasting of Day-Ahead Solar Power Generation from Granular Meteorological Features

Accurate forecasts for day-ahead photovoltaic (PV) power generation are crucial to support a high PV penetration rate in the local electricity grid and to assure stability in the grid. We use state-of-the-art tree-based machine learning methods to produce such forecasts and, unlike previous studies,...

詳細記述

書誌詳細

出版年:	Data Science in Science
主要な著者:	Nick Berlanger, Noah van Ophoven, Tim Verdonck, Ines Wilms
フォーマット:	論文
言語:	英語
出版事項:	Taylor & Francis Group 2024-12-01
主題:	Electricity markets forecasting machine learning regression trees renewable energy solar energy
オンライン･アクセス:	https://www.tandfonline.com/doi/10.1080/26941899.2024.2426786