An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation

Al-Alawi, S.M.; Al-Hinai, Hilal

doi:10.1016/s0960-1481(98)00068-8

Cited by 223 publications

(67 citation statements)

References 6 publications

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“…The MBE and RMSE values obtained were 0.12% and 5.67%, respectively. Alawi and Hinai [11] used ANN to predict solar radiation. The model used the following inputs: location parameters, month, and averages of pressure, temperature, vapor pressure, relative humidity, wind speed, and sunshine duration.…”

Section: Review On Ann Solar Radiation Modelsmentioning

confidence: 99%

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

Angela

Ssenyonga

James

2011

Advances in Artificial Neural Systems

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We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19 • N, a longitude of 32.34 • E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003-2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m 2 and a root mean square error of 0.521 MJ/m 2 . The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).

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Section: Review On Ann Solar Radiation Modelsmentioning

confidence: 99%

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

Angela

Ssenyonga

James

2011

Advances in Artificial Neural Systems

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“…Al-Alawi and Al-Hinai [4] discussed multi-layer feed forward network, back propagation (BP) training algorithm for global radiation prediction in Seeb, Oman. The inputs used in network were location, month, mean pressure, mean temperature, mean vapor pressure, mean relative humidity, mean wind speed and mean sunshine hours.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Compression of Solar Radiation Data Using Neural Networks

Mummadisetty¹,

Puri²,

Sharifahmadian³

et al. 2015

IJCNS

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The prediction of solar radiation is important for several applications in renewable energy research. There are a number of geographical variables which affect solar radiation prediction, the identification of these variables for accurate solar radiation prediction is very important. This paper presents a hybrid method for the compression of solar radiation using predictive analysis. The prediction of minute wise solar radiation is performed by using different models of Artificial Neural Networks (ANN), namely Multi-layer perceptron neural network (MLPNN), Cascade feed forward back propagation (CFNN) and Elman back propagation (ELMNN). Root mean square error (RMSE) is used to evaluate the prediction accuracy of the three ANN models used. The information and knowledge gained from the present study could improve the accuracy of analysis concerning climate studies and help in congestion control.

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“…Al-Alawi e Al-Hinai [42] empregaram uma rede MLP com 15 nodos na camada oculta para estimativa de valores médios mensais de radiação solar global, utilizando como dados de entrada os valores diários de pressão, temperatura, umidade relativa, velocidade do vento, duração de brilho do Sol e latitude da estação. O modelo desenvolvido apresentou boa capacidade de generalização, estimando a irradiação solar global para sítios não utilizados no treinamento com precisão da ordem de 95%.…”

Section: Aplicações Das Redes Neurais Artificiais Na Estimativa De Reunclassified

Aplicações de redes neurais e previsões de disponibilidade de recursos energéticos solares

Fiorin

Martins

Schuch

et al. 2011

Rev. Bras. Ensino Fís.

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Este trabalho tem como objetivo discutir de forma sucinta a ferramenta matemática conhecida como redes neurais artificiais e algumas aplicações naárea de energias renováveis. Inicialmente, o trabalho descreve a relevância desta ferramenta estatística nas diversasáreas do conhecimento e, posteriormente, conceitua e descreve as principais configurações possíveis de uma rede neural artificial. Por fim, o trabalho demonstra a aplicação da ferramenta para o levantamento de disponibilidade de recursos de energia solar no Brasil a partir de dados de superfície coletados em estações da rede SONDA operada pelo Centro de Ciência do Sistema Terrestre do Instituto Nacional de Pesquisas Espaciais. Os resultados obtidos até o momento mostram que as redes neurais podem fornecer estimativas com a confiabilidade necessária para avaliação da disponibilidade de energia solar e com melhor desempenho que outras técnicas estatísticas utilizadas na literatura. Palavras-chave: energia solar, redes neurais artificiais, modelagem atmosférica, modelos de mesoescala.This work aims at discussing the artificial neural networks (ANN) and some applications in renewable energy assessment. First, the paper describes the statistical relevance of this tool in different areas of knowledge and the main ANN concepts and configurations. Finally, the paper presents and discusses the use of ANN for the solar energy assessment in Brazil by using data collected in SONDA sites operated by the Center for Earth System Science of the Brazilian Institute for Space Research. The results show that ANN can provide reliable estimates with better performance than other statistical tools.

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An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation

Cited by 223 publications

References 6 publications

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

A Hybrid Method for Compression of Solar Radiation Data Using Neural Networks

Aplicações de redes neurais e previsões de disponibilidade de recursos energéticos solares

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