A novel interpretability machine learning model for wind speed forecasting based on feature and sub-model selection-Reference-Cited by-同舟云学术

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A novel interpretability machine learning model for wind speed forecasting based on feature and sub-model selection

Published:2024-12 Issue: Volume:255 Page:124560
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Shang Zhihao,Chen Yanhua^ORCID,Lai Daokai,Li Min,Yang Yi

Publisher

Elsevier BV

Reference75 articles.

1. Current status of hybrid structures in wind forecasting;Ahmadi;Engineering applications of artificial intelligence,2021

2. An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting;Aly;Sustainable Energy Technologies and Assessments,2020

3. Chen, L. and X. Lai. Comparison between ARIMA and ANN models used in short-term wind speed forecasting. in 2011 Asia-Pacific Power and Energy Engineering Conference. 2011. IEEE.

4. Wind speed and wind direction forecasting using echo state network with nonlinear functions;Chitsazan;Renewable energy,2019

5. A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting;da Silva;Energy,2021

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