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A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to...

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Autores principales: Rahimi, Negar, Park, Sejun, Choi, Wonseok, Oh, Byoungryul, Kim, Sookyung, Cho, Young-ho, Ahn, Sunghyun, Chong, Chulho, Kim, Daewon, Jin, Cheong, Lee, Duehee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9834683/
https://www.ncbi.nlm.nih.gov/pubmed/37521955
http://dx.doi.org/10.1007/s42835-023-01378-2
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author Rahimi, Negar
Park, Sejun
Choi, Wonseok
Oh, Byoungryul
Kim, Sookyung
Cho, Young-ho
Ahn, Sunghyun
Chong, Chulho
Kim, Daewon
Jin, Cheong
Lee, Duehee
author_facet Rahimi, Negar
Park, Sejun
Choi, Wonseok
Oh, Byoungryul
Kim, Sookyung
Cho, Young-ho
Ahn, Sunghyun
Chong, Chulho
Kim, Daewon
Jin, Cheong
Lee, Duehee
author_sort Rahimi, Negar
collection PubMed
description With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.
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spelling pubmed-98346832023-01-17 A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms Rahimi, Negar Park, Sejun Choi, Wonseok Oh, Byoungryul Kim, Sookyung Cho, Young-ho Ahn, Sunghyun Chong, Chulho Kim, Daewon Jin, Cheong Lee, Duehee J. Electr. Eng. Technol. Original Article With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed. Springer Nature Singapore 2023-01-12 2023 /pmc/articles/PMC9834683/ /pubmed/37521955 http://dx.doi.org/10.1007/s42835-023-01378-2 Text en © The Author(s) 2023, , corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Rahimi, Negar
Park, Sejun
Choi, Wonseok
Oh, Byoungryul
Kim, Sookyung
Cho, Young-ho
Ahn, Sunghyun
Chong, Chulho
Kim, Daewon
Jin, Cheong
Lee, Duehee
A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms
title A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms
title_full A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms
title_fullStr A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms
title_full_unstemmed A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms
title_short A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms
title_sort comprehensive review on ensemble solar power forecasting algorithms
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9834683/
https://www.ncbi.nlm.nih.gov/pubmed/37521955
http://dx.doi.org/10.1007/s42835-023-01378-2
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