This is an open access article distributed under the CC BY 4.0
Volume 19 article 830 pages: 578-585
Artificial neural network shows a good performance in predicting renewable energy. Many versions of Artificial Neural
Network (ANN) models have been implemented to predict solar potential. This study aims to determine the monthly
solar radiation in Semarang, Indonesia using ANN, and to visualize monthly solar irradiance as a map of the solar
system of Semarang. This research applied the perceptron multi-layer ANN model, with 7 variables as input data of
network learning, which were maximum temperature, relative humidity, wind speed, rainfall, longitude, latitude, and
elevation. The input data set was obtained from a NASA normalized geo-satellite database website with a 5-year average
daily score. Network training used backpropagation with one of the input layers, two of hidden layers, and one
of the output layer. The performance of the model during the analysis of mean absolute percentage error was highly
accurate (6.6%) when 12 and 10 neurons were respectively installed in the first and second hidden layers. The result
was presented in a monthly map of solar potential within the geographical information system (GIS) environment.
The result showed that ANN was able to be one of the alternatives to estimate solar irradiance data. The sun irradiance
map can be used by the government of Semarang City to provide information about the solar energy profile for
the implementation of the solar energy system.
1. Semarang Central Statistics Agency, “Semarang Municipality in Figures 2018,” 2018.
2. Indonesia, “Blueprint Pengelolaan Energi Nasional,” 2006.
3. H. Kutucu., A. Almryad. (2016). Modeling of Solar Energy Potential in Libya using an Artificial Neural Network Model. IEEE First International Conference on Data Stream Mining & Processing, pp. 356–359, DOI: 10.1109/DSMP.2016.7583575
4. P. Neelamegam., V. A. Amirtham. (2016). Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. Journal of Applied Research and Technology, vol. 14, no. 3, pp. 206–214, DOI: 10.1016/j.jart.2016.05.001
5. D. A. Fadare. (2009). Modelling of solar energy potential in Nigeria using an artificial neural network model. Applied Energy, vol. 86, no. 9, pp. 1410– 1422, DOI: 10.1016/j.apenergy.2008.12.005
6. Qazi., H. Fayaz., A. Wadi., R. Gopal., N. A. Rahim., W. Ahmed. (2015). The artificial neural network for solar radiation prediction and designing solar systems : a systematic literature review. Journal of Cleaner Production, vol. 104, pp. 1–12, DOI: 10.1016/j.jclepro.2015.04.041
7. O. N. Mensour., B. El Ghazzani., B. Hlimi., A. Ihlal. (2017). Modeling of solar energy potential in Souss-Massa area-Morocco using intelligence Artificial Neural Networks (ANNs). Energy Procedia, vol. 139, pp. 778–784, DOI: 10.1016/j.egypro.2017.11.287
8. E. F. Alsina., M. Bortolini., M. Gamberi., A. Regattieri. (2016). Artificial neural network optimisation for monthly average daily global solar radiation prediction. Energy Conversion and Management, vol. 120, pp. 320–329, DOI: 10.1016/j.enconman.2016.04.101
9. T. Khatib., A. Mohamed., K. Sopian., M. Mahmoud. (2012). Assessment of Artificial Neural Net¬works for Hourly Solar Radiation Prediction. Inter¬national Journal of Photoenergy, vol. 2012, DOI: 10.1155/2012/946890
10. M. Demirtas., M. Yesilbudak., Sagiroglu. (2012). Prediction of solar radiation using meteorological data. Proc Int Conf on Renewable Energy Research and Applications, Nagasaki, Japan 2012, pp. 1–5, DOI: 10.1109/ICRERA.2012.6477329
11. N. Celik., T. Muneer. (2013). Neural network based method for conversion of solar radiation data. Energy Convers Manage, vol. 67, no. 1, pp. 17–24, DOI: doi.org/10.1016/j.enconman.2012.11.010
12. S. Pereira., P. Canhoto., R. Salgado., M. João. (2019). Development of an ANN based corrective algorithm of the operational ECMWF global horizontal irradiation forecasts. Solar Energy, vol. 185, pp. 387– 405, DOI: doi.org/10.1016/j.solener.2019.04.070
13. M. Bou-rabee., S. A. Sulaiman., M. S. Saleh., S. Marafied. (2015). Using artificial neural networks to estimate solar radiation in Kuwait. Renewable and Sustainable Energy Reviews, vol. 72, pp. 434–438, DOI: 10.1016/j.rser.2017.01.013
14. B. Amrouche., X. Le Pivert. (2014). Artificial neural network based daily local forecasting for global solar radiation. Applied Energy, vol. 130, pp. 333–341, DOI: 10.1016/j.apenergy.2014.05.055
15. Assi., M. Al Shamisi., M. Jama. (2015). Prediction of Monthly Average Daily Global Solar Radiation in Al Ain City – UAE Using Artificial Neural Networks. Advances in Energy Planning, Environmental Education and Renewable Energy Sources.
16. M. Alluhaidah., S. Ieee., S. H. Shehadeh., S. Ieee., F. Ieee. (2014). Most Influential Variables for Solar Radiation Forecasting Using Artificial Neural Networks. 2014 Electrical Power and Energy Conference, pp. 71–75, DOI: 10.1109/EPEC.2014.36
17. P. Neelamegam., V. Arasu. (2016). Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. Revista Mexicana de Trastornos Alimentarios, vol. 14, no. 3, pp. 206–214, DOI: doi.org/10.1016/j.jart.2016.05.001
18. K. Ermis., A. Midilli., I. Dincer., M. A. Rosen. (2007). Artificial neural network analysis ofworld green energy use. Energy Policy, vol. 35, no. 17, pp. 31–43, DOI: 10.1016/j.enpol.2006.04.015
19. Ouammi., D. Zejli., H. Dagdougui., R. Benchrifa. (2012). Artificial neural network analysis of Moroccan solar potential. Renewable and Sustainable Energy Reviews, vol. 16, no. 7, pp. 4876–4889, DOI: 10.1016/j.rser.2012.03.071
20. M. Rumbayan., A. Abudureyimu., K. Nagasaka. (2012). Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system. Renewable and Sustainable Energy Reviews, vol. 16, no. 3, pp. 1437–1449, DOI: 10.1016/j.rser.2011.11.024
21. Sozen., E. Arcaklioglu., M. Ozalp. (2004). Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data. Energy Conversion and Management, vol. 45, pp. 3033–3052, DOI: 10.1016/j.enconman.2003.12.020
22. Y. Charabi., M. Ben., H. Rhouma., A. Gastli. (2010). GIS-Based Estimation of Roof-PV Capacity & Energy Production for the Seeb Region in Oman. 2010 IEEE International Energy Conference, vol. 2, pp. 41–44, DOI: 10.1109/ENERGYCON.2010.5771717
23. Mellit., Kalogirou. (2008). Artificial intelligence techniques for photovoltaic applications : A review. Progress in Energy and Combustion Science, vol. 34, pp. 574–632, DOI: 10.1016/j.pecs.2008.01.001Get rights and content
24. Mellit., A. M. Pavan. (2010). A 24-h forecast of solar irradiance using artificial neural networks: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy, vol. 84, no. 8, pp. 7–21, DOI: 10.1016/j.solener.2010.02.006
25. K. Yadav., S. S. Chandel. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable Energy, DOI: doi.org/10.1016/j. rser.2013.08.055