Open AccessArtificial Neural Network Performance Analysis for Solar Radiation Prediction, Case Study at Baron Techno Park
Author(s) -
Ridwan Budi Prasetyo,
Haidar Rahman,
Ikrima Alfi,
Fredi Prima Sakti
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/997/1/012019
Subject(s) - renewable energy , artificial neural network , solar energy , mean squared error , solar power , government (linguistics) , meteorology , computer science , environmental economics , environmental science , engineering , operations research , artificial intelligence , statistics , mathematics , power (physics) , geography , electrical engineering , economics , physics , linguistics , philosophy , quantum mechanics
As stated in Government Regulation No. 79 of 2014 on National Energy Policy (KEN), the New and Renewable Energy (NRE) mix target is at least 23% by 2025. Now the utilization of solar energy in Indonesia has only reached about 0.05% or 100 MW. Government compiles a roadmap for the use of solar energy that targets the installed PV mini-grid capacity until 2025 is 6.5 GW. Technically, before installing a solar power plant, solar potential data is needed for a certain period of time. This is absolutely necessary considering the potential for solar is intermittent. The solar data are then processed to create a model forecasting so that it can optimize the resulting energy output. Forecasting using artificial intelligence with artificial neural network algorithms is a good solution because it has higher accuracy. To see the comparison of the performance of the ANN of this research with previous research. The finding shows that Baron 1-7-1 performed better than 2P 1-2-1 and Baron 1-2-1, regardless that the RMSE have a slight difference but still Baron 1-7-1 outperformed the others, with the best value of RMSE 0.15185 and R2 of 0.88996.