Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing-Reference-Cited by-同舟云学术

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Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

Published:2020-06 Issue: Volume:268 Page:115023
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Theocharides Spyros,Makrides George,Livera Andreas,Theristis Marios,Kaimakis Paris,Georghiou George E.

Funder

European Union

Cyprus Research Promotion Foundation

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference64 articles.

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2. Solar forecasting methods for renewable energy integration;Inman;Prog Energy Combust Sci,2013

3. Assessment of forecasting techniques for solar power production with no exogenous inputs;Pedro;Sol Energy,2012

4. Solar power prediction based on satellite images and support vector machine;Jang;IEEE Trans Sustain Energy,2016

5. Wolff B, Kramer O, Heinemann D. Selection of numerical weather forecast features for PV power predictions with random forests. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2017. https://doi.org/10.1007/978-3-319-50947-1_8.

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