Artificial Neural Network Parameter Optimization: Improving Meteorological Data Predictions through Machine Learning

Year 2024, Volume: 4 Issue: 1, 53 - 61, 30.08.2024

Ceyhun Kapucu , Oğuz Akpolat

https://doi.org/10.54569/aair.1535217

Abstract

This study aims to create an artificial neural network (ANN) based model to predict solar irradiance using open-sourced meteorological data. A neural network that is feed-forward with backpropagation was employed to build the model. A large combination of model parameters including learning algorithms, transfer functions, number of hidden layers, and neurons was used to customize the neural network. The data used in this study is a part of the publicly available dataset containing real outdoor measurements provided by The National Renewable Energy Laboratory (NREL). The proposed model has been validated by measuring prediction errors using normalized mean squared error (NMSE) and prediction accuracies using regression value (R). The lowest value of the NMSE error was obtained with a neural network model based on three hidden layers employing 40, 8, and 5 neurons respectively. The R-value of this model was the highest among all models. The results have shown that the ascending/descending distribution of neurons in hidden layers is an important factor among other parameters.

Keywords

Artificial neural network, solar irradiance, meteorological modeling, curve fitting

References

• A. Sözen, E. Arcaklioǧlu, M. Özalp, and E. G. Kanit, “Use of artificial neural networks for mapping of solar potential in Turkey,” Applied Energy, vol. 77, no. 3, pp. 273–286, Mar. 2004.
• O. Şenkal and T. Kuleli, “Estimation of solar radiation over Turkey using artificial neural network and satellite data,” Applied Energy, vol. 86, no. 7–8, pp. 1222–1228, 2009.
• A. Koca, H. F. Oztop, Y. Varol, and G. O. Koca, “Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey,” Expert Systems with Applications, vol. 38, no. 7, pp. 8756–8762, 2011.
• Z. Wang, F. Wang, and S. Su, “Solar irradiance short-term prediction model based on BP neural network,” Energy Procedia, vol. 12, pp. 488–494, 2011.
• B. Marion et al., “Data for Validating Models for PV Module Performance,” 2014.
• M. Ozgoren, M. Bilgili, and B. Sahin, “Estimation of global solar radiation using ANN over Turkey,” Expert Systems with Applications, vol. 39, no. 5, pp. 5043–5051, 2012.
• C. Renno, F. Petito, and A. Gatto, “ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building,” Journal of Cleaner Production, vol. 135, pp. 1298–1316, 2016.
• M. Bou-Rabee, S. A. Sulaiman, M. S. Saleh, and S. Marafi, “Using artificial neural networks to estimate solar radiation in Kuwait,” Renewable and Sustainable Energy Reviews, vol. 72, no. November 2016, pp. 434–438, 2017.
• X. Xue, “Prediction of daily diffuse solar radiation using artificial neural networks,” International Journal of Hydrogen Energy, vol. 42, no. 47, pp. 28214–28221, 2017.
• F. Rodríguez, A. Fleetwood, A. Galarza, and L. Fontán, “Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control,” Renewable Energy, vol. 126, pp. 855–864, 2018.
• “Matlab 2017b,” 2017. [Online]. Available: https://ww2.mathworks.cn/en/. [Accessed: 04-May-2019].
• Ç. Elmas, Yapay Zeka Uygulamaları Yapay Sinir Ağları – Bulanık Mantık– Genetik Algoritma, 4th ed. 2011.
• S. Haykin, Neural Networks and Learning Machines, 3d Edition, 3rd ed. ew Jersey: Pearson Education, 2008.
• “Photovoltaic Research, NREL,” 2019. [Online]. Available: https://www.nrel.gov/pv/index.html. [Accessed: 07-May-2019].
• B. Marion et al., “New data set for validating PV module performance models,” in 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), 2014, pp. 1362–1366.
• C. Kubat, MATLAB Yapay Zeka ve Mühendislik Uygulamaları. Abaküs, 2019.
• G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, vol. 103, no. 4. New York, NY: Springer New York, 2013.