A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning-Reference-Cited by-同舟云学术

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A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning

Published:2020-01 Issue: Volume:145 Page:2426-2434
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Wang Gang,Jia Ru,Liu Jinhai,Zhang Huaguang

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference15 articles.

1. A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data;Sharifiana;Renew. Energy,2018

2. A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining;Xu;IEEE Trans. Sustain. Energy,2015

3. Short-term wind power forecasts by a synthetical similar time series data mining method;Sun;Renew. Energy,2018

4. Wind speed and direction forecasting for wind power generation using ARIMA model;Yatiyana,2017

5. Wind speed forecasting using hybrid ANN-Kalman Filter techniques;Diksha,2012

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