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Hourly global solar radiation prediction based on seasonal and stochastic feature.

作者信息

Li You, Wang Yafei, Qian Hui, Gao Weijun, Fukuda Hiroatsu, Zhou Weisheng

机构信息

Dual-carbon Research Center, Hangzhou City University, Hangzhou, 310015, China.

Asia-Japan Research Institute, Ritsumeikan University, Ibaraki 567-8570, Japan.

出版信息

Heliyon. 2023 Sep 6;9(9):e19823. doi: 10.1016/j.heliyon.2023.e19823. eCollection 2023 Sep.

DOI:10.1016/j.heliyon.2023.e19823
PMID:37809907
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10559210/
Abstract

Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/ae7798fb295a/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/6aef7e48d0ae/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/a0662069aeed/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/c05f88e24d05/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/bf5ffbc5b1a8/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/0badca1df747/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/30a13878a2d6/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/cf29b34b4668/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/14e74c420b5a/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/ae7798fb295a/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/6aef7e48d0ae/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/a0662069aeed/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/c05f88e24d05/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/bf5ffbc5b1a8/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/0badca1df747/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/30a13878a2d6/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/cf29b34b4668/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/14e74c420b5a/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/547d/10559210/ae7798fb295a/fx1.jpg

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本文引用的文献

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Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review.使用经验方法和人工智能方法进行太阳辐射预测:比较综述
Heliyon. 2023 Jun 7;9(6):e17038. doi: 10.1016/j.heliyon.2023.e17038. eCollection 2023 Jun.
2
A state of art review on estimation of solar radiation with various models.关于使用各种模型估算太阳辐射的技术现状综述。
Heliyon. 2023 Jan 21;9(2):e13167. doi: 10.1016/j.heliyon.2023.e13167. eCollection 2023 Feb.
3
Prediction of site-specific solar diffuse horizontal irradiance from two input variables in Colombia.
新兴市场与工业化市场之间随机季节性、一月效应及市场效率的比较分析。
Heliyon. 2024 Mar 24;10(7):e28301. doi: 10.1016/j.heliyon.2024.e28301. eCollection 2024 Apr 15.
基于两个输入变量对哥伦比亚特定地点太阳漫射水平辐照度的预测。
Heliyon. 2021 Dec 16;7(12):e08602. doi: 10.1016/j.heliyon.2021.e08602. eCollection 2021 Dec.