Estimation of solar radiation in Australia from rainfall and temperature observations

Liu, De Li; Scott, Bill

doi:10.1016/s0168-1923(00)00173-8

Cited by 176 publications

(122 citation statements)

References 18 publications

Supporting

Mentioning

104

Contrasting

Unclassified

Order By: Relevance

“…Both Liu & Scott (2001) and Podestá et al (2004) observed that the models considering precipitation as a binary variable had a similar result compared to those which used rainfall data (mm) as an input.…”

Section: Resultsmentioning

confidence: 84%

“…Liu & Scott (2001) estimated solar radiation in different cities in Australia using temperature and precipitation data. Their root mean square error (RMSE) values were between 2.01 and 5.44 MJ m -2 d -1 .…”

Section: Resultsmentioning

confidence: 99%

“…All of them included three neuron layers, input, hidden and output layer (Figure 1). Each model was built with a different number of neurons in the input layer (E i ) (Table 1), which received as input patterns, the following daily values: extraterrestrial radiation (ETR, in MJ m -2 d -1 ), which is calculated on the basis of latitude and the day of the year, according to Chassériaux (1990), and considers a solar constant of 1,370 W m -2 , adjusted by the earth's position; square root of the temperature range (SRTR, ºC ½ ), following the procedure used by Liu & Scott (2001). The precipitation was considered in two different ways, as a potential function with exponent 0.05 (PP 0.05 , mm 0.05 ), according to Alonso et al (2002), and as a binary function with value 1 for occurrence and zero for days with no precipitation (PP bin ) (Liu & Scott, 2001).…”

Section: Methodsmentioning

confidence: 99%

“…Each model was built with a different number of neurons in the input layer (E i ) (Table 1), which received as input patterns, the following daily values: extraterrestrial radiation (ETR, in MJ m -2 d -1 ), which is calculated on the basis of latitude and the day of the year, according to Chassériaux (1990), and considers a solar constant of 1,370 W m -2 , adjusted by the earth's position; square root of the temperature range (SRTR, ºC ½ ), following the procedure used by Liu & Scott (2001). The precipitation was considered in two different ways, as a potential function with exponent 0.05 (PP 0.05 , mm 0.05 ), according to Alonso et al (2002), and as a binary function with value 1 for occurrence and zero for days with no precipitation (PP bin ) (Liu & Scott, 2001). This representation does not mean an important loss of information, since according to our data, in 95% of the cases with precipitation, the values of the potential function differ from the unit in less than 0.15; cloudiness observed at 14 hours local time (C 14 oktas) and the relative sunshine duration (h/H%, in which h is the sunshine duration, and H is the day duration, both in hours).…”

Section: Methodsmentioning

confidence: 99%

“…Solar radiation affects crop growth and numerical models are used to estimate soil humidity, photosynthesis, and potential evapotranspiration (Ball et al, 2004). Solar radiation is an infrequently measured meteorological variable, compared to temperature and rainfall (Liu & Scott, 2001;Weiss & Hays, 2004); there are many agricultural regions of Argentina lacking data on radiation and, thus, they have to be estimated (Grossi Gallegos, 1998). To calculate the solar radiation that reaches the earth's surface, empirical and physical models can be used (Noia et al, 1993a(Noia et al, , 1993bFlores Tovar & Baldasano, 2001).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

Bocco

Ovando

2006

Pesq. agropec. bras.

View full text Add to dashboard Cite

-The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m -2 d -1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.Index terms: modelling, prediction, backpropagation neural networks. Desenvolvimento e avaliação de modelos de redes neurais para estimação da irradiação solar diária em Córdoba, ArgentinaResumo -O objetivo deste trabalho foi desenvolver modelos de redes neuronais, do tipo retropropagação, para a estimação da irradiação solar, a partir de dados de irradiação solar extraterrestre, amplitude térmica, precipitação, nebulosidade e razão de insolação. O treinamento e a validação foram realizados com dados correspondentes a Córdoba, Argentina. O comportamento e ajuste entre os valores observados e os estimados pelas redes foram avaliados para diferentes combinações das variáveis de entrada, que apresentaram valores do erro quadrático médio entre 3,15 e 3,88 MJ m -2 d -1 . Este último valor corresponde ao modelo que calcula a irradiação somente utilizando precipitação e amplitude térmica diária. Os resultados exibem em todos os modelos um ajuste apropriado ao comportamento sazonal da irradiação solar e permitem concluir a pertinência e o adequado desempenho desse método para estimar fenômenos complexos como a irradiação solar.Termos para indexação: modelagem, predição, redes neurais de retropropagação.

show abstract

Section: Resultsmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

Bocco

Ovando

2006

Pesq. agropec. bras.

View full text Add to dashboard Cite

show abstract

Estimation of the solar radiation based on air temperature in tunisia

Ayadi

Driss

Bouabidi

et al. 2018

Env Prog and Sustain Energy

View full text Add to dashboard Cite

This article focuses on the estimation of the solar global daily radiation using the values of the air temperature. The reason for choosing this environmental parameter is that the data of the air temperature are available worldwide and are easy to be recorded from the local meteorological station. Specified models were developed to estimate the global solar radiation data in considered cities in Tunisia, North Africa. A site‐independent model was proposed to estimate the solar radiation using only the minimum and the maximum daily air temperature. By using five statistical indicators, the proposed models were assessed in order to present the best model for each region in Tunisia. Acceptable values of the indicators were recorded. The presented statistical test is based on recorded data from meteorological stations implemented in Tunis, Kairouan, Sfax, Gabes, and Kebili. The choice of the five selected cities is based on the availability of the metrological measurements. The assessment of the potential of the solar energy in the considered cities was presented. Zoning the assessment of the solar radiation would give a better understanding of the distribution of solar radiation and an overview of the general solar radiation climatic conditions within Tunisia. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 600–607, 2019

show abstract

Model data selection using gamma test for daily solar radiation estimation

Remesan

Shamim

Han

2008

Hydrological Processes

View full text Add to dashboard Cite

Abstract:Hydrological modelling is a complicated procedure and there are many tough questions facing all modellers: what input data should be used? how much data is required? and what model should be used? In this paper, the gamma test (GT) has been used for the first time in modelling one of the key hydrological components: solar radiation. The study aimed to resolve the questions about the relative importance of input variables and to determine the optimum number of data points required to construct a reliable smooth model. The proposed methodology has been studied through the estimation of daily solar radiation in the Brue Catchment, the UK. The relationship between input and output in the meteorological data sets was achieved through error variance estimation before the modelling using the GT. This work has demonstrated how the GT helps model development in nonlinear modelling techniques such as local linear regression (LLR) and artificial neural networks (ANN). It was found that the GT provided very useful information for input data selection and subsequent model development. The study has wider implications for various hydrological modelling practices and suggests further exploration of this technique for improving informed data and model selection, which has been a difficult field in hydrology in past decades.

show abstract

Estimation of solar radiation in Australia from rainfall and temperature observations

Cited by 176 publications

References 18 publications

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

Estimation of the solar radiation based on air temperature in tunisia

Model data selection using gamma test for daily solar radiation estimation

Contact Info

Product

Resources

About