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All Journal International Journal of Electrical and Computer Engineering JURNAL PENGABDIAN KEPADA MASYARAKAT Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Jurnal Solusi Masyarakat Dikara
Sukarwoto, Sukarwoto
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Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Development and assessment of solar radiation forecasting models based on operational data Suwarno, Suwarno; Cahyadi, Catra Indra; Sukarwoto, Sukarwoto; Napitupulu, Janter
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4838-4845

Abstract

Operational forecasting of solar radiation is critical for better decision-making by solar energy system operators, due to the variability of energy resources and demand. Although the numerical weather forecasting (NWP) model can predict solar radiation variables, there are often significant errors, especially in direct normal irradiation (DNI), which are influenced by the type and concentration of aerosols and clouds. This paper presents an artificial neural network (ANN) based method to generate operational DNI forecasts using weather and aerosol forecast data from the European Center for medium-range weather forecasts (ECMWF) and Copernicus atmospheric monitoring service (CAMS) respectively. The ANN model is designed to predict weather and aerosol variables at a certain time as input, while other models use the DNI forecast improvement period before the instant forecast. The model was developed using North Sumatra location observations and obtained DNI forecasting results every 10 minutes on the first day with DNI forecasting compared to the initial forecasting which was scaled down with the R2, mean absolute error (MAE), and relative mean square error (RMSE) models were 0.6753, 151.2, and 210.2 W/m2, so that and provides good agreement with experimental data.
Co-Authors Akbar, Muhammad Caesar Arnawan Hasibuan Caesar Akbar, Muhammad Cahyadi, Catra Indra Napitupulu, Janter Nur, Fauziah Pasa, Inda Tri Siregar, Muhammad Amril Siregar, Widyana Verawaty Suwarno Suwarno Tegar Amalillah, Caesario Warassari, Betia Wardani, Ivana Wimatra, Ayub