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Accurate total solar irradiance estimates under irradiance measurements scarcity scenarios

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Abstract

Accurate estimates of total global solar irradiance reaching the Earth’s surface are relevant since routine measurements are not always available. This work aimed to determine which of the models used to estimate daily total global solar irradiance (TGSI) is the best model when irradiance measurements are scarce in a given site. A model based on an artificial neural network (ANN) and empirical models based on temperature and sunshine measurements were analyzed and evaluated in Córdoba, Argentina. The performance of the models was benchmarked using different statistical estimators such as the mean bias error (MBE), the mean absolute bias error (MABE), the correlation coefficient (r), the Nash-Sutcliffe equation (NSE), and the statistics t test (t value). The results showed that when enough measurements were available, both the ANN and the empirical models accurately predicted TGSI (with MBE and MABE ≤ |0.11| and ≤ |1.98| kWh m−2 day−1, respectively; NSE ≥ 0.83; r ≥ 0.95; and |t values| < t critical value). However, when few TGSI measurements were available (2, 3, 5, 7, or 10 days per month) only the ANN-based method was accurate (|t value| < t critical value), yielding precise results although only 2 measurements per month were available for 1 year. This model has an important advantage over the empirical models and is very relevant to Argentina due to the scarcity of TGSI measurements.

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Acknowledgments

We thank Secretaría de Ciencia y Tecnología de la Universidad Nacional de Córdoba (UNC), Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), and Agencia Nacional de Promoción Científica (FONCYT) for their support.

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Authors and Affiliations

  1. Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000, Córdoba, Argentina

    María Laura López

  2. Instituto de Física Enrique Gaviola (IFEG)/CONICET, Ciudad Universitaria, 5000, Córdoba, Argentina

    María Laura López

  3. Facultad de Ciencias Químicas, Departamento de Físico Química, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000, Córdoba, Argentina

    Luis E. Olcese, Gustavo G. Palancar & Beatriz M. Toselli

  4. Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC)/CONICET/CLCM, Ciudad Universitaria, 5000, Córdoba, Argentina

    Luis E. Olcese, Gustavo G. Palancar & Beatriz M. Toselli

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  1. María Laura López
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  2. Luis E. Olcese
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  3. Gustavo G. Palancar
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Correspondence to María Laura López.

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Highlights

• Performance of models to estimate total global solar irradiance (TGSI) is assessed.

• Frequency of measurements needed to calibrate models is analyzed.

• A minimum number of TGSI measurements per month are enough for ANN training.

• When TGSI measurements are scarce, empirical methods (EM) are not accurate.

• An ANN model is an overriding option with respect to the analyzed EM if TGSI measurements are scarce.

Appendix

Appendix

The tables presented in this section show the weights found for the ANN model when 2, 3, 5, 7, and 10 days per month were used for the calibration/training processes (Tables 6, 7, 8, 9, and 10, respectively).

Table 6 Weights of the ANN model when 2 days per month were used for the calibration/validation processes
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Table 7 Weights of the ANN model when 3 days per month were used for the calibration/validation processes
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Table 8 Weights of the ANN model when 5 days per month were used for the calibration/validation processes
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Table 9 Weights of the ANN model when 7 days per month were used for the calibration/validation processes
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Table 10 Weights of the ANN model when 10 days per month were used for the calibration/validation processes
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López, M.L., Olcese, L.E., Palancar, G.G. et al. Accurate total solar irradiance estimates under irradiance measurements scarcity scenarios. Environ Monit Assess 191, 568 (2019). https://doi.org/10.1007/s10661-019-7742-3

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