Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

According to the World Economic Outlook (WEO), the global demand for energy is presumably going to be increased due to growing the world’s population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a result of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), relative root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R2). Finally, simulation results provided that the best result is found by the DNN model.

Keywords:

Daily global solar radiation forecasting, artificial neural network deep neural network, renewable energy,

PDF

___

[1] Başaran Filik, Ü., Filik, T., & Gerek, Ö. N. A hysteresis model for fixed and sun tracking solar PV power generation systems. Energies; 2018, p. 603.
[2] Alzahrani, A., Shamsi, P., Dagli, C. and Ferdowsi, M. Solar irradiance forecasting using deep neural networks. Procedia Computer Science; 2017, p. 304-313.
[3] Benali, L., Notton, G., Fouilloy, A., Voyant, C., & Dizene, R. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components. Renewable Energy; 2019, p. 871–884.
[4] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. Time series analysis: forecasting and control. John Wiley & Sons; 2015.
[5] Chaturvedi, D. K. Solar Power Forecasting: A Review, 145(6); 2016, p. 28–50.
[6] Collares-Pereira, M., & Rabl, A. The average distribution of solar radiationcorrelations between diffuse and hemispherical and between daily and hourly insolation values. Solar Energy, 22(2), 1979, p. 155–164.
[7] Gueymard, C. Mean daily averages of beam radiation received by tilted surfaces as affected by the atmosphere. Solar Energy, 37(4), p. 1986, 261–267.
[8] Http-2. date of access: July 5, 2019, from http://www.visualgenedeveloper.net/; 2019.
[9] Jain, P. C. Comparison of techniques for the estimation of daily global irradiation and a new technique for the estimation of hourly global irradiation. Solar & Wind Technology, 1(2), 1984, p. 123–134.
[10] Jain, P. C. Estimation of monthly average hourly global and diffuse irradiation. Solar & Wind Technology, 5(1), 1988, p. 7–14.
[11] Kalogirou, S. A. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews, 5(4), 2001, p. 373–401.
[12] Liu, B. Y., & Jordan, R. C. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Solar Energy, 4(3), 1960, p. 1–19.
[13] Ryu, S., Noh, J., & Kim, H. Deep neural network-based demand side short term load forecasting. Energies, 10(1), 3, 2017.
[14] Ayvazoğluyüksel, Ö. and Filik Başaran Ü. Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskişehir. Renewable and Sustainable Energy Reviews, 91, 2018, p. 639-653
[15] Atique, S., Noureen, S., Roy, V., Subburaj, V., Bayne, S., & Macfie, J. Forecasting of total daily solar energy generation using ARIMA: A case study. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), 2019, p. 114-119.
[16] Sivhugwana, K. S., & Ranganai, E. Intelligent techniques, harmonically coupled and SARIMA models in forecasting solar radiation data: A hybridisation approach. Journal of Energy in Southern Africa, 31(3), 2020, p. 14-37.
[17] Craggs, C., Conway, E. and Pearsall N. M. Stochastic modelling of solar irradiance on horizontal and vertical planes at a northerly location. Renewable Energy 18; 1999, p. 445-463
[18] Soubdhan, T., Voyant, C., & Lauret, P. Influence of Global Solar Radiation Typical Days on Forecasting Models Error. The Third Southern African Solar Energy Conference (SASEC2015); 2015.
[19] Sun, Y. Deep Neural Network Regression and Sobol Sensitivity Analysis for Daily Solar Energy Prediction Given Weather Data. Purdue University; 2018.
[20] Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 2017, p. 569–582.