Title :
New approach on renewable energy solar power prediction in Indonesia based on Artificial Neural Network technique: Southern region of Sulawesi island study case
Author :
Prastawa, Andhika ; Dalimi, Rinaldy
Author_Institution :
Center for Energy Conversion & Conservation Technol., Agency for the Assessment & Applic. of Technol., Jakarta, Indonesia
Abstract :
Indonesia located at 94°-141°E and 6°N-11°S is the largest archipelago in the equator on earth. As a tropical country, Indonesia is endowed with abundant solar energy potential. This study is focused on modeling the Global Solar Radiation using Artificial Neural Network to predict GSR in a location which is available with meteorological data but lack with radiation measurement data. A case study on 5 locations in South Western region of Sulawesi was used to develop the model. The ANN model used 4 location with 5 years monthly meteorological and radiation data for training, and one location for testing. The simulation shows that an ANN with 4 layers and 5 neurons is the most appropriate model with an MSE of 0.003 and r of 0.99937. The model provides an excellent performance of prediction of with an MPE of 0.1427% and r2 of 0.999967. The predicted radiation data is in reasonable agreement with the actual data at the testing location; this shows the ability of ANN technique in generalization of data unavailability and produces an accurate prediction.
Keywords :
mean square error methods; neural nets; power engineering computing; solar power; sunlight; ANN model; ANN technique; GSR; Indonesia; MSE; South Western region; Sulawesi; Sulawesi island; artificial neural network; artificial neural network technique; data unavailability generalization; global solar radiation; meteorological data; radiation data; radiation measurement data; renewable energy solar power prediction; solar energy potential; testing location; Artificial neural networks; Atmospheric modeling; Data models; Predictive models; Renewable energy sources; Solar energy; Solar radiation; Artificial Neural Network; Back Propagation; Feed Forward; Global Solar Radiation; Mean Square Error; Renewable Energy;
Conference_Titel :
QiR (Quality in Research), 2013 International Conference on
Conference_Location :
Yogyakarta
Print_ISBN :
978-1-4673-5784-5
DOI :
10.1109/QiR.2013.6632558