Generation of daily global solar irradiation with support vector machines for regression

Antoñanzas-Torres, F.; Urraca, Rubén; Antonanzas, J.; Ceniceros, Julio Fernández; Martínez-de-Pisón, Francisco Javier

doi:10.1016/j.enconman.2015.02.086

Cited by 61 publications

(24 citation statements)

References 35 publications

(45 reference statements)

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“…Linear H/H 0 = a + b (n/N) Angström [4] and Prescott [5] Quadratic H/H 0 = a + b (n/N) + c (n/N) 2 Akinoglu and Ecevit [6] Cubic 3 Bahel et al [29] Logarithmic H/H 0 = a + b. log (n/N) Ampratwum and Dorvlo…”

Section: Regression Equations Sourcementioning

confidence: 99%

“…To overcome this lack of information on the solar energy potential, several solar energy models using different approaches such as regression models, satellite images, artificial neural networks (ANN), fuzzy genetics and support vector machines have been proposed in the literature to estimate the solar radiation [3]. However, the most widely used models to estimate the global solar radiation are empirical models which typically aims to relate the atmospheric transmittance to the extraterrestrial radiation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solar radiation mapping using sunshine duration-based models and interpolation techniques: Application to Tunisia

Chelbi

Gagnon

Waewsak

2015

Energy Conversion and Management

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Section: Regression Equations Sourcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solar radiation mapping using sunshine duration-based models and interpolation techniques: Application to Tunisia

Chelbi

Gagnon

Waewsak

2015

Energy Conversion and Management

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“…SVM has gained prominence in many fields of knowledge to solve complex problems of pattern recognition, classification, regression analysis and forecasting [33][34][35].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Assessment of ANN and SVM models for estimating normal direct irradiation (Hb)

Santos

Escobedo

Teramoto

et al. 2016

Energy Conversion and Management

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a b s t r a c tThis study evaluates the estimation of hourly and daily normal direct irradiation (H b ) using machine learning techniques (ML): Artificial Neural Network (ANN) and Support Vector Machine (SVM). Time series of different meteorological variables measured over thirteen years in Botucatu were used for training and validating ANN and SVM. Seven different sets of input variables were tested and evaluated, which were chosen based on statistical models reported in the literature. Relative Mean Bias Error (rMBE), Relative Root Mean Square Error (rRMSE), determination coefficient (R 2 ) and ''d" Willmott index were used to evaluate ANN and SVM models. When compared to statistical models which use the same set of input variables (R 2 between 0.22 and 0.78), ANN and SVM show higher values of R 2 (hourly models between 0.52 and 0.88; daily models between 0.42 and 0.91). Considering the input variables, atmospheric transmissivity of global radiation (kt), integrated solar constant (H sc ) and insolation ratio (n/N, n is sunshine duration and N is photoperiod) were the most relevant in ANN and SVM models. The rMBE and rRMSE values in the two time partitions of SVM models are lower than those obtained with ANN. Hourly ANN and SVM models have higher rRMSE values than daily models. Optimal performance with hourly models was obtained with ANN4 h (rMBE = 12.24%, rRMSE = 23.99% and ''d" = 0.96) and SVM4 h (rMBE = 1.75%, rRMSE = 20.10% and ''d" = 0.96). Optimal performance with daily models was obtained with ANN2 d (rMBE = À3.09%, rRMSE = 18.95% and ''d" = 0.97) and SVM2 d (rMBE = 0.60%, rRMSE = 19.39% and ''d" = 0.97). ANN and SVM models improved H b estimations as compared with other results from the literature. SVM has better performance than ANN to estimate H b , and it should be the first option of choice.

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“…Therefore, a user-supplied penalty parameter is unnecessary and the automatic method for generating parsimonious models is simplified. In the last year, this methodology has been applied successfully to other fields [12][13][14].…”

Section: Introductionmentioning

confidence: 96%

GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Sanz-García

Ceniceros

Antoñanzas-Torres

et al. 2015

Applied Soft Computing

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Generation of daily global solar irradiation with support vector machines for regression

Cited by 61 publications

References 35 publications

Solar radiation mapping using sunshine duration-based models and interpolation techniques: Application to Tunisia

Solar radiation mapping using sunshine duration-based models and interpolation techniques: Application to Tunisia

Assessment of ANN and SVM models for estimating normal direct irradiation (Hb)

GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

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