Estimativa da irradiação solar global pelo método de Angstrom-Prescott e técnicas de aprendizado de máquinas
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Data
2016-02-22
Autores
Silva, Maurício Bruno Prado da [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
No presente trabalho é descrito o estudo comparativo de métodos de estimativas da irradiação solar global (HG) nas partições diária (HGd) e mensal (HGm): geradas pela técnica de Angstrom-Prescott (A-P) e duas técnicas de Aprendizado de Máquina (AM), Máquinas de Vetores de Suporte (MVS) e Redes Neurais Artificiais (RNA). A base de dados usada foi medida no período de 1996 a 2011, na Estação Solarimétrica em Botucatu. Por meio da regressão entre a transmissividade atmosférica (HG/HO) e razão de insolação (n/N), o modelo estatístico (A-P) foi determinado, obtendo equações lineares que permitem estimar HG com elevados coeficientes de determinação. As técnicas, MVS e RNA, foram treinadas na mesma arquitetura de A-P (modelo 1). As técnicas MVS e RNA foram treinadas ainda em mais 3 modelos com acréscimos, uma a uma, das variáveis temperatura do ar, precipitação e umidade relativa (modelos 2, 3 e 4). Os modelos foram validados usando uma base de dados de dois anos, denominadas de típico e atipico, por meio de correlações entre os valores estimados e medidos, indicativos estatísticos rMBE, MBE, rRMSE, RMSE e d de Willmott. Os indicativos estatísticos r das correlações mostraram que o modelo (A-P) pode estimar HG com elevados coeficientes de determinação nas duas condições de validação. Já indicativos estatísticos rMBE, MBE, rRMSE, RMSE e d de Willmott indicam que o modelo (A-P) pode ser utilizado na estimativa de HGd com exatidão e precisão. Os indicativos estatísticos obtidos pelos 4 modelos das técnicas MVSd e RNAd (diária) e MVSm e RNAm (mensal) podem ser utilizadas nas estimativas de HGd com elevadas correlações e com precisão e exatidão. Entre os modelos foram selecionadas por comparação entre os indicativo estatisticos as redes MVS4d e RNA4d (diária) e MVS1m e RNA1m (mensal). A comparação dos indicativos estatísticos rMBE, MBE, rRMSE, RMSE, d de Willmott, r e R2 obtidos na validação entre os modelos (A-P), MVS e RNA mostrou que: a técnica MVS apresentou melhor resultado que o modelo estatístico de (A-P); esta técnica apresentou melhor resultado que a RNA; o modelo estatístico (A-P), apresentou no geral melhor resultado que a RNA.
In this paper describes the comparative study of different methods for estimating global solar irradiation (HG) in the daily partitions (HGd) and monthly (HGm): generated by Angstrom-Prescott (AP) and two machine learning techniques (ML), Support Vector Machines (SVM) and Artificial Neural Networks (ANN). The used database was measured from 1996 to 2011, in Solarimetric station in Botucatu. Through regression between atmospheric transmissivity (HG / HO) and insolation ratio (n / N), the statistical model (A-P) was determined, obtaining linear equations that allow estimating HG with high coefficients of determination. The techniques, svm and ANN, were trained on the same architecture of A-P (model 1). The SVM and ANN techniques were further trained on the most models with 3 additions, one by one, the variable air temperature, rainfall and relative humidity (model 2, 3 and 4 ). The models were validated using a database of two years, called of typical and atypical, with correlation between estimated and measured values, statistical indications: rMBE, MBE, rRMSE, RMSE, and d Willmott. The statistical indicative of correlations coefficient (r) showed that the model (A-P) can be estimated with high HG determination coefficients in the two validation conditions. The rMBE, MBE, rRMSE, RMSE Willmott and d indicate that the model (A-P) can be used to estimate HGD with accuracy and precision. The statistical indicative obtained by the four models of technical SVMd and ANNd (daily) and SVMm and ANNm (monthly) can be used in the estimates of HGD with high correlations and with precision and accuracy. Among the models were selected by comparing the indicative statistical SVM4d and ANN4d networks (daily) and SVM1m and ANN1m (monthly). The comparison of statistical indicative rMBE, MBE, rRMSE, RMSE, d Willmott, r and R2 obtained in the validation of the models (A-P), SVM and ANN showed that: the SVM technique showed better results than the statistical model (A-P); this technique showed better results than the ANN; the statistical model (A-P) showed overall better result than ANN.
In this paper describes the comparative study of different methods for estimating global solar irradiation (HG) in the daily partitions (HGd) and monthly (HGm): generated by Angstrom-Prescott (AP) and two machine learning techniques (ML), Support Vector Machines (SVM) and Artificial Neural Networks (ANN). The used database was measured from 1996 to 2011, in Solarimetric station in Botucatu. Through regression between atmospheric transmissivity (HG / HO) and insolation ratio (n / N), the statistical model (A-P) was determined, obtaining linear equations that allow estimating HG with high coefficients of determination. The techniques, svm and ANN, were trained on the same architecture of A-P (model 1). The SVM and ANN techniques were further trained on the most models with 3 additions, one by one, the variable air temperature, rainfall and relative humidity (model 2, 3 and 4 ). The models were validated using a database of two years, called of typical and atypical, with correlation between estimated and measured values, statistical indications: rMBE, MBE, rRMSE, RMSE, and d Willmott. The statistical indicative of correlations coefficient (r) showed that the model (A-P) can be estimated with high HG determination coefficients in the two validation conditions. The rMBE, MBE, rRMSE, RMSE Willmott and d indicate that the model (A-P) can be used to estimate HGD with accuracy and precision. The statistical indicative obtained by the four models of technical SVMd and ANNd (daily) and SVMm and ANNm (monthly) can be used in the estimates of HGD with high correlations and with precision and accuracy. Among the models were selected by comparing the indicative statistical SVM4d and ANN4d networks (daily) and SVM1m and ANN1m (monthly). The comparison of statistical indicative rMBE, MBE, rRMSE, RMSE, d Willmott, r and R2 obtained in the validation of the models (A-P), SVM and ANN showed that: the SVM technique showed better results than the statistical model (A-P); this technique showed better results than the ANN; the statistical model (A-P) showed overall better result than ANN.
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Palavras-chave
Radiação solar, Angtrom-Prescott, Modelagem estatística, Solar radiation, Angstrom-Prescott, Statistical Modeling