Solar power generation prediction based on deep Learning-Reference-Cited by-同舟云学术

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Solar power generation prediction based on deep Learning

Published:2021-10 Issue: Volume:47 Page:101354
ISSN:2213-1388
Container-title:Sustainable Energy Technologies and Assessments
language:en
Short-container-title:Sustainable Energy Technologies and Assessments

Author:

Chang Rui,Bai Lei,Hsu Ching-Hsien

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference31 articles.

1. A deep learning model for short-term power load and probability density forecasting;Guo;Energy,2018

2. Haupt, S. E., Kosović, B., Jensen, T., Lazo, J. K., Lee, J. A., Jiménez, P. A., ... & Miller, S. (2018). Building the Sun4Cast system: Improvements in solar power forecasting.Bulletin of the American Meteorological Society,99(1), 121-136.

3. A hybrid forecasting method for solar output power based on variational mode decomposition, deep belief networks and auto-regressive moving average;Xie;Applied Sciences,2018

4. Short-term load forecasting based on elastic net improved GMDH and difference degree weighting optimization;Liu;Applied Sciences,2018

5. Wind Speed Prediction Model Using LSTM and 1D-CNN;Fukuoka;Journal of Signal Processing,2018

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