Inter-relationship between mean-daily irradiation and temperature, and decomposition models for hourly irradiation and temperature

Gago, Eulalia Jadraque; Pereira, Octavio; Tham, Yieng Wei; Aldali, Yasser; Muneer, Tariq

doi:10.1093/ijlct/ctq039

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…This variable is more available and gives relatively better results than other variables [26]. However, air temperature data can be used instead, when the sunshine duration is not available at a specific location [27] as air temperature data have been collected at many locations globally and generally for a much longer period relative to any meteorological records [28].…”

Section: Introductionmentioning

confidence: 99%

Lipid production of Chlorella vulgaris and Pseudokirchneriella subcapitata

Gonçalves

Pires

Simões

2013

Int J Energy Environ Eng

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Accurate and precisess estimation of spatio-temporal variability of solar radiation is critical. Some commonly used models evaluate this variability using methods in which the data required for estimating atmospheric attenuation may not be easily accessible for some study areas. Here, a daily solar radiation estimation method which uses ambient air temperature, a Digital Elevation Model, time of year, and monthly radiation estimates from Solar Analyst model has been proposed. The objective was to use air temperature-based empirical models for atmospheric transmissivity and diffuse fractions to vary total monthly radiation estimation from Solar Analyst, and then calculate total daily radiation as a fraction of total monthly radiation by applying a daily transmissivity-based ratio, as air temperature data are readily available at most locations on the planet. Results revealed that daily solar radiation can be estimated very well, with Mean Absolute Bias Error of around 40-53 W m −2 or Mean Bias Error of ± 10%, under all sky conditions at seven sites in diverse climate regions, using significantly less input data. The presented method is an improvement over previously used methods with Mean Bias Error of under 10% but more input parameters. Furthermore, the hourly solar radiation values can be calculated using the presented method using the ratio between daily and hourly radiation, for example from literature values and estimated daily insolation. The result also showed that the method is more useful for those stations with substantially higher numbers of sunny days than cloudy or partly cloudy days because the uncertainty of the model decreased from cloudy to sunny sky conditions. The implemented Digital Elevation Models environment of this method makes it applicable in many studies that need spatial estimation of solar radiation, especially for solar energy generation projects.

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Section: Introductionmentioning

confidence: 99%

Lipid production of Chlorella vulgaris and Pseudokirchneriella subcapitata

Gonçalves

Pires

Simões

2013

Int J Energy Environ Eng

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“…where Tmax and Tmin is the maximum and minimum temperature of each day; kRS is the adjustment coefficient. Since Nottingham and Dezhou is the "interior region", kRS is taken as 0.16 [39].…”

Section: Applicationsmentioning

confidence: 99%

Component-dependent thermal properties of molten salt eutectics for solar thermal energy storage: Experiments, molecular simulation and applications

Zhang

Wang

et al. 2022

Applied Thermal Engineering

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“…Then according to (Gago et al, 2010) we found that there is one inter relation between Solar Irradiation (IRR) and Ambient (Amb) Temperature, which made this possible to perform z-test and compare both the years data to conclude the result.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…1 Performing Z-test separately on Solar Irradiation and Ambient Temperature which will help us decide the Alternative Hypothesis is greater than Null Hypothesis. 2 After determining the Null and Alternative Hypothesis we will calculate energy with a relation given by (Gago et al,2010). 3 After calculating the energies produced by both the years we will compare the variation difference in mean values of energies within 5 percent in range.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Solor Irradiation Analysis Using Prescriptive Analysis

Singh¹,

Nanduri²,

Sobti³

et al. 2020

IJEAST

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Solar Irradiation Analysis using Prescriptive Analysis is a design for an ideal solution, built on the user's needs to analyze Solar Power Plants. It helps in delivering flawless operations on the data provided from the power plants, and predicts volatility in performance by providing probable reason of up-gradation/degradation in efficiency. It focuses on the idea of Sustainable Energy Resources as the future needs to be more Eco-friendly. Solar Irradiation Analysis using Prescriptive Analysis is the combination of hardware (which includes solar panels, invertors, connections, and other devices) and an Intuitive software (Coded in Python, using Data Analytics Techniques). Data Analytics which includes Descriptive Analysis, Predictive Analysis and Prescriptive Analysis is secure, reliable, and compatible with all kinds of data. The results are generated in the form of graphs and charts which can be useful in the long term for the future studies based on Solar Irradiation, and in the short term they provided better energy generation of Users.

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Inter-relationship between mean-daily irradiation and temperature, and decomposition models for hourly irradiation and temperature

Cited by 7 publications

References 21 publications

Lipid production of Chlorella vulgaris and Pseudokirchneriella subcapitata

Lipid production of Chlorella vulgaris and Pseudokirchneriella subcapitata

Component-dependent thermal properties of molten salt eutectics for solar thermal energy storage: Experiments, molecular simulation and applications

Solor Irradiation Analysis Using Prescriptive Analysis

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