Accurate total solar irradiance estimates under irradiance measurements scarcity scenarios
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.
KeywordsArtificial neural network Scarce measurements Solar energy Solar radiation estimation
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.
- De Souza, J. L., Bastos Lyra, G., Dos Santos, C. M., Araujo Ferreira Junior, R., Tiba, C., Bastos Lyra, G., & Maringolo Lemes, M. A. (2016). Empirical models of daily and monthly global solar irradiation using sunshine duration for Alagoas State, Northeastern Brazil. Sustainable Energy Technologies and Assessments, 14, 35–45.CrossRefGoogle Scholar
- Donatelli, M., & Campbell, G. S. (1998). A simple model to estimate global solar radiation. Proceedings of the fifth European society of agronomy congress, Nitra, Slovak Republic (pp. 133–134).Google Scholar
- Duffie, J. A., & Beckman, W. A. (1991). Solar engineering of thermal processes. Hoboken: Wiley.Google Scholar
- Hargreaves, G. H., & Samani, Z. A. (1982). Estimating potential evapotranspiration. Journal of Irrigation and Drainage Engineering, 108, 223–230.Google Scholar
- Newland, F. J. (1988). A study of solar radiation models for the coastal region of South China. Solar Energy, 31, 227–235.Google Scholar
- Prescott, J. A. (1940). Evaporation from a water surface in relation to solar radiation. Transactions of the Royal Society of South Australia, 64, 114–118.Google Scholar
- Sharifi, S. S., Rezaverdinejad, V., & Nourani, V. (2016). Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: a comparative study of selected temperature-based approaches. Journal of Atmospheric and Solar - Terrestrial Physics, 149, 131–145.CrossRefGoogle Scholar
- Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability & statistics for engineers & scientists. Boston: Prentice Hall.Google Scholar
- Widén, J., Carpman, N., Castellucci, V., Lingfors, D., Olauson, J., Remouit, F., Bergkvist, M., Grabbe, M., & Waters, R. (2015). Variability assessment and forecasting of renewables: a review for solar, wind, wave and tidal resources. Renewable and Sustainable Energy Reviews, 44, 356–375.CrossRefGoogle Scholar