Evidence map›Paper›PMID 37809907›Full record

ArticleHeliyon2023

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

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

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

You LiDual-carbon Research Center, Hangzhou City University, Hangzhou, 310015, China.
Yafei WangFaculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu, 808-0135, Japan.
Hui QianDual-carbon Research Center, Hangzhou City University, Hangzhou, 310015, China.
Weijun GaoFaculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu, 808-0135, Japan.
Hiroatsu FukudaFaculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu, 808-0135, Japan.
Weisheng ZhouCollege of Policy Science, Ritsumeikan University, Ibaraki, 567-8570, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Hourly solar radiation forecastLayer-by-layer weakening modelRegression analysisSeasonal and stochastic featureSurface meteorological parameters

Identifiers

PMID37809907
PMCPMC10559210

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.