Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms-Reference-Cited by-同舟云学术

Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms

Published:2021-11 Issue: Volume:177 Page:181-192
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Lv Jiaqing,Zheng Xiaodong,Pawlak Mirosław,Mo Weike,Miśkowicz Marek

Funder

Narodowe Centrum Nauki

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference54 articles.

1. Towards fully renewable energy systems: experience and trends in Denmark;Pinson;CSEE journal of power and energy systems,2017

2. Key technologies for integration of multitype renewable energy sources—research on multi-timeframe robust scheduling/dispatch;Chen;IEEE Transactions on Smart Grid,2015

3. Integration of renewable energy systems and challenges for dynamics, control, and automation of electrical power systems;Sajadi;Wiley Interdisciplinary Reviews: Energy Environ.,2019

4. Wind Power Forecasting: State-Of-The-Art 2009;Monteiro,2009

5. Dynamic Factor Graphs-A New Wind Power Forecasting Approach;Silva,2014

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